Python deap.creator.create() 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 saveImage(gen, polygonData):

    # only every 100 generations:
    if gen % 100 == 0:

        # create folder if does not exist:
        folder = "images/results/run-{}-{}".format(POLYGON_SIZE, NUM_OF_POLYGONS)
        if not os.path.exists(folder):
            os.makedirs(folder)

        # save the image in the folder:
        imageTest.saveImage(polygonData,
                            "{}/after-{}-gen.png".format(folder, gen),
                            "After {} Generations".format(gen))

# Genetic Algorithm flow: 

def eval_func(individual, points):
    # Transform the tree expression in a callable function
    func = toolbox.compile(expr=individual)

    # Evaluate the mean squared error
    mse = ((func(x) - (2 * x**3 - 3 * x**2 + 4 * x - 1))**2 for x in points)

    return math.fsum(mse) / len(points),

# Function to create the toolbox 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("max", numpy.max)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = elitism.eaSimpleWithElitism(population,
                                                      toolbox,
                                                      cxpb=P_CROSSOVER,
                                                      mutpb=P_MUTATION,
                                                      ngen=MAX_GENERATIONS,
                                                      stats=stats,
                                                      halloffame=hof,
                                                      verbose=True)

    # print best solution found:
    print("- Best solution is: ")
    print("params = ", test.formatParams(hof.items[0]))
    print("Accuracy = %1.5f" % hof.items[0].fitness.values[0])

    # extract statistics:
    maxFitnessValues, meanFitnessValues = logbook.select("max", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(maxFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Max / Average Fitness')
    plt.title('Max and Average fitness over Generations')
    plt.show() 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("min", numpy.min)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = elitism.eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print best solution found:
    best = hof.items[0]
    print("-- Best Individual = ", best)
    print("-- Best Fitness = ", best.fitness.values[0])
    print()
    print("-- Schedule = ")
    nsp.printScheduleInfo(best)

    # extract statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(minFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Min / Average Fitness')
    plt.title('Min and Average fitness over Generations')
    plt.show() 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("min", np.min)
    stats.register("avg", np.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = elitism.eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print hall of fame members info:
    print("- Best solutions are:")
    for i in range(HALL_OF_FAME_SIZE):
        print(i, ": ", hof.items[i].fitness.values[0], " -> ", hof.items[i])

    # plot statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")
    plt.figure(1)
    sns.set_style("whitegrid")
    plt.plot(minFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Min / Average Fitness')
    plt.title('Min and Average fitness over Generations')

    # plot best solution:
    sns.set_style("whitegrid", {'axes.grid' : False})
    nQueens.plotBoard(hof.items[0])

    # show both plots:
    plt.show() 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("min", numpy.min)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = elitism.eaSimpleWithElitism(population,
                                                      toolbox,
                                                      cxpb=P_CROSSOVER,
                                                      mutpb=P_MUTATION,
                                                      ngen=MAX_GENERATIONS,
                                                      stats=stats,
                                                      halloffame=hof,
                                                      verbose=True)

    # print best solution:
    best = hof.items[0]
    print()
    print("Best Solution = ", best)
    print("Best Fitness = ", best.fitness.values[0])

    # save best solution for a replay:
    car.saveActions(best) 

def randomFloat(low, up):
    return [random.uniform(l, u) for l, u in zip([low] * NUM_OF_PARAMS, [up] * NUM_OF_PARAMS)]

# create an operator that randomly returns a float in the desired range: 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("max", numpy.max)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = elitism.eaSimpleWithElitism(population,
                                                      toolbox,
                                                      cxpb=P_CROSSOVER,
                                                      mutpb=P_MUTATION,
                                                      ngen=MAX_GENERATIONS,
                                                      stats=stats,
                                                      halloffame=hof,
                                                      verbose=True)

    # print best solution found:
    best = hof.items[0]
    print()
    print("Best Solution = ", best)
    print("Best Score = ", best.fitness.values[0])
    print()

    # save best solution for a replay:
    cartPole.saveParams(best)

    # find average score of 100 episodes using the best solution found:
    print("Running 100 episodes using the best solution...")
    scores = []
    for test in range(100):
        scores.append(cart_pole.CartPole().getScore(best))
    print("scores = ", scores)
    print("Avg. score = ", sum(scores) / len(scores)) 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("max", numpy.max)
    stats.register("avg", numpy.mean)

    # perform the Genetic Algorithm flow:
    population, logbook = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION, ngen=MAX_GENERATIONS,
                                   stats=stats, verbose=True)


    # Genetic Algorithm is done - extract statistics:
    maxFitnessValues, meanFitnessValues = logbook.select("max", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(maxFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Max / Average Fitness')
    plt.title('Max and Average Fitness over Generations')
    plt.show() 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("min", numpy.min)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = elitism.eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                                      ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print best solution found:
    best = hof.items[0]
    print("-- Best Ever Individual = ", best)
    print("-- Best Ever Fitness = ", best.fitness.values[0])

    # extract statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(minFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Min / Average Fitness')
    plt.title('Min and Average fitness over Generations')
    plt.show() 

def main():

    # create initial population (generation 0):
    population = toolbox.populationCreator(n=POPULATION_SIZE)

    # prepare the statistics object:
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("max", numpy.max)
    stats.register("avg", numpy.mean)

    # define the hall-of-fame object:
    hof = tools.HallOfFame(HALL_OF_FAME_SIZE)

    # perform the Genetic Algorithm flow with hof feature added:
    population, logbook = elitism.eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                                      ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print best solution found:
    print("- Best solutions are:")
    for i in range(HALL_OF_FAME_SIZE):
        print(i, ": ", hof.items[i], ", fitness = ", hof.items[i].fitness.values[0],
              ", accuracy = ", zoo.getMeanAccuracy(hof.items[i]), ", features = ", sum(hof.items[i]))

    # extract statistics:
    maxFitnessValues, meanFitnessValues = logbook.select("max", "avg")

    # plot statistics:
    sns.set_style("whitegrid")
    plt.plot(maxFitnessValues, color='red')
    plt.plot(meanFitnessValues, color='green')
    plt.xlabel('Generation')
    plt.ylabel('Max / Average Fitness')
    plt.title('Max and Average fitness over Generations')
    plt.show() 

def randomFloat(low, up):
    return [random.uniform(l, u) for l, u in zip([low] * NUM_OF_PARAMS, [up] * NUM_OF_PARAMS)]

# create an operator that randomly returns a float in the desired range: 

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 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():
    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 setup_func_single_obj():
    creator.create(FITCLSNAME, base.Fitness, weights=(-1.0,))
    creator.create(INDCLSNAME, list, fitness=creator.__dict__[FITCLSNAME]) 

def setup_func_multi_obj():
    creator.create(FITCLSNAME, base.Fitness, weights=(-1.0, -1.0))
    creator.create(INDCLSNAME, list, fitness=creator.__dict__[FITCLSNAME]) 

def setup_func_multi_obj_numpy():
    creator.create(FITCLSNAME, base.Fitness, weights=(-1.0, -1.0))
    creator.create(INDCLSNAME, numpy.ndarray, fitness=creator.__dict__[FITCLSNAME]) 

def setUp(self):

        @binary.bin2float(0, 1023, 10)
        def evaluate(individual):
            """Simplest evaluation function."""
            return individual

        creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
        creator.create("Individual", list, fitness=creator.FitnessMin)
        self.toolbox = base.Toolbox()
        self.toolbox.register("evaluate", evaluate) 

def setUp(self):
        creator.create("FitnessMax", base.Fitness, weights=(1.0,))
        creator.create("IndList", list, fitness=creator.FitnessMax)
        creator.create("IndArray", array.array,  typecode='f', fitness=creator.FitnessMax)
        creator.create("IndNDArray", numpy.ndarray,  typecode='f', fitness=creator.FitnessMax)
        creator.create("IndTree", gp.PrimitiveTree, fitness=creator.FitnessMax)
        self.toolbox = base.Toolbox()
        self.toolbox.register("func", func)
        self.toolbox.register("lambda_func", lambda: "True") 

def test_attribute():
    creator.create(CNAME, list, a=1)
    l = creator.__dict__[CNAME]([1,2,3,4])

    assert l.a == 1, "%s, expected %i" % (l.a, 1) 

def test_array():
    creator.create(CNAME, array.array, typecode="i")
    a = creator.__dict__[CNAME]([1,2,3,4])
    b = creator.__dict__[CNAME]([5,6,7,8])

    a[1:3], b[1:3] = b[1:3], a[1:3]
    ta = array.array("i", [1,6,7,4])
    tb = array.array("i", [5,2,3,8])
    assert a == ta, "%s, expected %s" % (a, ta)
    assert b == tb, "%s, expected %s" % (b, tb) 

def test_numpy_nocopy():
    creator.create(CNAME, numpy.ndarray)
    a = creator.__dict__[CNAME]([1,2,3,4])
    b = creator.__dict__[CNAME]([5,6,7,8])

    a[1:3], b[1:3] = b[1:3], a[1:3]
    ta = numpy.array([1,6,7,4])
    tb = numpy.array([5,6,7,8])
    assert all(a == ta), "%s, expected %s" % (a, ta)
    assert all(b == tb), "%s, expected %s" % (b, tb) 

def test_numpy_copy():
    creator.create(CNAME, numpy.ndarray)
    a = creator.__dict__[CNAME]([1,2,3,4])
    b = creator.__dict__[CNAME]([5,6,7,8])

    a[1:3], b[1:3] = b[1:3].copy(), a[1:3].copy()
    ta = numpy.array([1,6,7,4])
    tb = numpy.array([5,2,3,8])
    assert all(a == ta), "%s, expected %s" % (a, ta)
    assert all(b == tb), "%s, expected %s" % (b, tb) 

def randomFloat(low, up):
    return [random.uniform(l, u) for l, u in zip([low] * DIMENSIONS, [up] * DIMENSIONS)]

# create an operator that randomly returns a float in the desired range and dimension: 

def _setup_memory(self):
        """Setup Memory object for memory caching.
        """
        if self.memory:
            if isinstance(self.memory, str):
                if self.memory == "auto":
                    # Create a temporary folder to store the transformers of the pipeline
                    self._cachedir = mkdtemp()
                else:
                    if not os.path.isdir(self.memory):
                        try:
                            os.makedirs(self.memory)
                        except:
                            raise ValueError(
                                'Could not create directory for memory caching: {}'.format(self.memory)
                            )
                    self._cachedir = self.memory

                self._memory = Memory(location=self._cachedir, verbose=0)
            elif isinstance(self.memory, Memory):
                self._memory = self.memory
            else:
                raise ValueError(
                    'Could not recognize Memory object for pipeline caching. '
                    'Please provide an instance of joblib.Memory,'
                    ' a path to a directory on your system, or \"auto\".'
                ) 

def randomFloat(low, up):
    return [random.uniform(a, b) for a, b in zip([low] * DIMENSIONS, [up] * DIMENSIONS)]

# create an operator that randomly returns a float in the desired range and dimension: 

def randomFloat(low, up):
    return [random.uniform(l, u) for l, u in zip([low] * DIMENSIONS, [up] * DIMENSIONS)]

# create an operator that randomly returns a float in the desired range and dimension: