Python Trajectory.f_iter_runs方法代码示例

# 需要导入模块: from pypet import Trajectory [as 别名]
# 或者: from pypet.Trajectory import f_iter_runs [as 别名]
def main():

    # No environment here ;-)
    filename = os.path.join('experiments', 'example_20.hdf5')
    traj = Trajectory('onemax', filename=filename, overwrite_file=True)


    # ------- Add parameters ------- #
    traj.f_add_parameter('popsize', 100)
    traj.f_add_parameter('CXPB', 0.5)
    traj.f_add_parameter('MUTPB', 0.2)
    traj.f_add_parameter('NGEN', 20)

    traj.f_add_parameter('generation', 0)
    traj.f_add_parameter('ind_idx', 0)
    traj.f_add_parameter('ind_len', 50)

    traj.f_add_parameter('indpb', 0.005)
    traj.f_add_parameter('tournsize', 3)

    traj.f_add_parameter('seed', 42)
    traj.f_store(only_init=True)


    # ------- Create and register functions with DEAP ------- #
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("Individual", list, fitness=creator.FitnessMax)

    toolbox = base.Toolbox()
    # Attribute generator
    toolbox.register("attr_bool", random.randint, 0, 1)
    # Structure initializers
    toolbox.register("individual", tools.initRepeat, creator.Individual,
        toolbox.attr_bool, traj.ind_len)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)

    # Operator registering
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutFlipBit, indpb=traj.indpb)
    toolbox.register("select", tools.selTournament, tournsize=traj.tournsize)
    toolbox.register("evaluate", eval_wrapper)

    pool = multip.Pool(4)
    toolbox.register("map", pool.map)  # We use the pool's map function!


    # ------- Initialize Population -------- #
    random.seed(traj.seed)

    pop = toolbox.population(n=traj.popsize)
    CXPB, MUTPB, NGEN = traj.CXPB, traj.MUTPB, traj.NGEN


    start_idx = 0  # We need to count executed runs

    print("Start of evolution")
    for g in range(traj.NGEN):
        print("-- Generation %i --" % g)

        # Determine individuals that need to be evaluated
        eval_pop = [ind for ind in pop if not ind.fitness.valid]

        # Add as many explored runs as individuals that need to be evaluated
        traj.f_expand(cartesian_product({'generation': [g], 'ind_idx': range(len(eval_pop))}))

        # We need to make the storage service multiprocessing safe
        mc = MultiprocContext(traj, wrap_mode='QUEUE')
        mc.f_start()

        # Create a single iterable to be passed to our fitness function (wrapper).
        # `yields='copy'` is important, the pool's `map` function will
        # go over the whole iterator at once and store it in memory.
        # So for every run we need a copy of the trajectory.
        # Alternatively, you could use `yields='self'` and use the pool's `imap` function.
        zip_iterable = izip(traj.f_iter_runs(start_idx, yields='copy'), eval_pop)

        fitnesses = toolbox.map(eval_wrapper, zip_iterable)
        # fitnesses is just a list of tuples [(fitness,), ...]
        for idx, fitness in enumerate(fitnesses):
            # Update fitnesses
            eval_pop[idx].fitness.values = fitness

        # Finalize the multiproc wrapper
        mc.f_finalize()

        # Update start index
        start_idx += len(eval_pop)

        print("  Evaluated %i individuals" % len(eval_pop))

        # Gather all the fitnesses in one list and print the stats
        fits = [ind.fitness.values[0] for ind in pop]

        length = len(pop)
        mean = sum(fits) / length
        sum2 = sum(x*x for x in fits)
        std = abs(sum2 / length - mean**2)**0.5

        print("  Min %s" % min(fits))
        print("  Max %s" % max(fits))
#.........这里部分代码省略.........