Python creator.create方法代码示例

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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) 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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: 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
def generate_dag(optimal_indvidual, stage_name, num_nodes):
    # create nodes for the graph
    nodes = np.empty((0), dtype=np.str)
    for n in range(1, (num_nodes + 1)):
        nodes = np.append(nodes, ''.join([stage_name, "_", str(n)]))

    # initialize directed asyclic graph (DAG) and add nodes to it
    dag = DAG()
    for n in nodes:
        dag.add_node(n)

    # split best indvidual found via GA to identify vertices connections and connect them in DAG
    edges = np.split(optimal_indvidual, np.cumsum(range(num_nodes - 1)))[1:]
    v2 = 2
    for e in edges:
        v1 = 1
        for i in e:
            if i:
                dag.add_edge(''.join([stage_name, "_", str(v1)]), ''.join([stage_name, "_", str(v2)]))
            v1 += 1
        v2 += 1

    # delete nodes not connected to anyother node from DAG
    for n in nodes:
        if len(dag.predecessors(n)) == 0 and len(dag.downstream(n)) == 0:
            dag.delete_node(n)
            nodes = np.delete(nodes, np.where(nodes == n)[0][0])

    return dag, nodes 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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)) 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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) 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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\".'
                ) 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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: 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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: 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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 elitism:
    population, logbook = elitism.eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

    # print info for best solution found:
    best = hof.items[0]
    print("-- Best Individual = ", best)
    print("-- Best 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() 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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 = algorithms.eaSimple(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,
                                              ngen=MAX_GENERATIONS, stats=stats, halloffame=hof, verbose=True)

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

    # plot best solution:
    plt.figure(1)
    tsp.plotData(best)

    # plot statistics:
    minFitnessValues, meanFitnessValues = logbook.select("min", "avg")
    plt.figure(2)
    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')

    # show both plots:
    plt.show() 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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 = algorithms.eaSimple(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])

    print("-- Knapsack Items = ")
    knapsack.printItems(best)

    # 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() 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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: ",
          test.formatParams(hof.items[0]),
          ", accuracy = ",
          hof.items[0].fitness.values[0]) 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
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: \n",
          test.formatParams(hof.items[0]),
          "\n => accuracy = ",
          hof.items[0].fitness.values[0]) 

# 需要导入模块: from deap import creator [as 别名]
# 或者: from deap.creator import create [as 别名]
def createParticle():
    particle = creator.Particle(np.random.uniform(MIN_START_POSITION,
                                                  MAX_START_POSITION,
                                                  DIMENSIONS))
    particle.speed = np.random.uniform(MIN_SPEED, MAX_SPEED, DIMENSIONS)
    return particle

# create the 'particleCreator' operator to fill up a particle instance: