Python Environment.add_postprocessing方法代码示例

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

    filename = os.path.join('hdf5', 'FiringRate.hdf5')
    env = Environment(trajectory='FiringRate',
                      comment='Experiment to measure the firing rate '
                            'of a leaky integrate and fire neuron. '
                            'Exploring different input currents, '
                            'as well as refractory periods',
                      add_time=False, # We don't want to add the current time to the name,
                      log_stdout=True,
                      log_config='DEFAULT',
                      multiproc=True,
                      ncores=2, #My laptop has 2 cores ;-)
                      wrap_mode='QUEUE',
                      filename=filename,
                      overwrite_file=True)

    traj = env.trajectory

    # Add parameters
    add_parameters(traj)

    # Let's explore
    add_exploration(traj)

    # Ad the postprocessing function
    env.add_postprocessing(neuron_postproc)

    # Run the experiment
    env.run(run_neuron)

    # Finally disable logging and close all log-files
    env.disable_logging()

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

    env = Environment(trajectory='postproc_deap',
                      overwrite_file=True,
                      log_stdout=False,
                      log_level=50,  # only display ERRORS
                      automatic_storing=True,  # Since we us post-processing, we
                      # can safely enable automatic storing, because everything will
                      # only be stored once at the very end of all runs.
                      comment='Using pypet and DEAP with less overhead'
                      )
    traj = env.traj


    # ------- Add parameters ------- #
    traj.f_add_parameter('popsize', 100, comment='Population size')
    traj.f_add_parameter('CXPB', 0.5, comment='Crossover term')
    traj.f_add_parameter('MUTPB', 0.2, comment='Mutation probability')
    traj.f_add_parameter('NGEN', 20, comment='Number of generations')

    traj.f_add_parameter('generation', 0, comment='Current generation')
    traj.f_add_parameter('ind_idx', 0, comment='Index of individual')
    traj.f_add_parameter('ind_len', 50, comment='Length of individual')

    traj.f_add_parameter('indpb', 0.005, comment='Mutation parameter')
    traj.f_add_parameter('tournsize', 3, comment='Selection parameter')

    traj.f_add_parameter('seed', 42, comment='Seed for RNG')


    # Placeholders for individuals and results that are about to be explored
    traj.f_add_derived_parameter('individual', [0 for x in range(traj.ind_len)],
                                 'An indivudal of the population')
    traj.f_add_result('fitnesses', [], comment='Fitnesses of all individuals')


    # ------- 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)


    # ------- Initialize Population and Trajectory -------- #
    random.seed(traj.seed)
    pop = toolbox.population(n=traj.popsize)

    eval_pop = [ind for ind in pop if not ind.fitness.valid]
    traj.f_explore(cartesian_product({'generation': [0],
                                     'ind_idx': range(len(eval_pop)),
                                     'individual':[list(x) for x in eval_pop]},
                                        [('ind_idx', 'individual'),'generation']))

    # ----------- Add postprocessing ------------------ #
    postproc = Postprocessing(pop, eval_pop, toolbox)  # Add links to important structures
    env.add_postprocessing(postproc)

    # ------------ Run applying post-processing ---------- #
    env.run(eval_one_max)

    # ------------ Finished all runs and print result --------------- #
    print("-- End of (successful) evolution --")
    best_ind = tools.selBest(pop, 1)[0]
    print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))