deap实战_2017中国数学建模大赛_B题_第二题

来源：互联网发布：mac bluestack模拟器编辑：程序博客网时间：2024/06/05 00:19

简介

补充:本例子仅仅是之前deap类库的一个实战例子,所以先别问我数学建模的事情,我暂时不想回答(还有为毛踩我文章…..我本来就不是写数学建模的……╮(╯▽╰)╭)(2017/10/31)

原问题是给出一个定价策略,证明其相较于原来定价策略的优点.

那么首先我们第一题第二问得到了一个价格-完成率函数,此时我们需要的是给出一个新的定价函数,并利用遗传算法得到最佳参数.

思路

编码–>我们需要编码的是定价函数的参数
评价函数—->将编码输入的定价函数得到价格,然后将价格输入之前得到的价格-完成率函数得到完成率
求解的目标应当是最大化完成率
为了控制成本需要对价格进行一定的限制,避免为了提高完成率,而过高定价
阅读提示,建议阅读该部分前,阅读该基础文章http://blog.csdn.net/fontthrone/article/details/78253230

Types

import randomfrom deap import basefrom deap import creatorfrom deap import toolsimport timeThresholdValue = 28.6670026583creator.create("FitnessMax", base.Fitness, weights=(1.0,))  # 定义最大化适应度creator.create("Individual", list, fitness=creator.FitnessMax)  # 这里的list种群的数据类型toolbox = base.Toolbox()# Attribute generator: define 'attr_bool' to be an attribute ('gene')#                      which corresponds to integers sampled uniformly#                      from the range [0,1] (i.e. 0 or 1 with equal#                      probability)toolbox.register("attr_bool", random.random)  # 包含了0,1的随机整数,初始化种群# Structure initializers: define 'individual' to be an individual#                         consisting of 100 'attr_bool' elements ('genes')toolbox.register("individual", tools.initRepeat, creator.Individual,                 toolbox.attr_bool, 5)# define the population to be a list of 'individual'stoolbox.register("population", tools.initRepeat, list, toolbox.individual)

ReadData

读取数据,为之后的计算做准备

import pandas as pddf2 = pd.read_csv('/home/fonttian/Data/MM2017/db.csv')npones = df2['npones']level = df2['level']length = df2['length']MiDu = df2['MiDu']npjt = df2['npjt']listlen = df2['listlen']price = df2['price']

定义评价函数(适应度函数)

def evalOneMax(individual):    newPrice = (individual[0] - 0.5) * npones * 20 + (individual[1] - 0.5) * length * 20 + (individual[2] - 0.5) * MiDu * 20 + (individual[3] - 0.5) * level * 20 + (individual[4]) * 100 +listlen * 65.7    # npones,nplength,npMiDuList3    w2 = [-0.01633732, 0.83539635, -0.06544261, -0.00280863]    xjb = length * w2[0] + level * w2[1] + MiDu * w2[2] + npjt * w2[3]    xjb = xjb * newPrice    sums = 0    for i in range(len(xjb)):        # yuzhi = 28.6670026583        # if xjb[i] >= yuzhi and newPrice[i] <= price[i] *1.1: # 608        # if xjb[i] >= yuzhi: # 655        # yuzhi = 0.474373718686        if xjb[i] >= ThresholdValue and sum(newPrice) <= 57707.5 * 1.3: # 655            sums += 1        else:            pass    return sums, # 注意最后的 , 该文件中必须要有,不然会报错

注册其他参数(Operator registration)

# ---------------------Operator registration---------------------toolbox.register("evaluate", evalOneMax)toolbox.register("mate", tools.cxTwoPoint)toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)toolbox.register("select", tools.selTournament, tournsize=3)

设计运行程序,获取我们想要的结果

def main():    random.seed(64)    # hash(64)is used    # random.seed方法的作用是给随机数对象一个种子值，用于产生随机序列。    # 对于同一个种子值的输入，之后产生的随机数序列也一样。    # 通常是把时间秒数等变化值作为种子值，达到每次运行产生的随机系列都不一样    # create an initial population of 300 individuals (where    # each individual is a list of integers)    pop = toolbox.population(n=300)  # 定义300个个体的种群    # CXPB  is the probability with which two individuals    #       are crossed    #    # MUTPB is the probability for mutating an individual    #    # NGEN  is the number of generations for which the    #       evolution runs   迭代次数    CXPB, MUTPB, NGEN = 0.5, 0.2, 100    print("Start of evolution")    # Evaluate the entire population    fitnesses = list(map(toolbox.evaluate, pop))    # for ind, fit in zip(pop, fitnesses):    #     ind.fitness.values = fit    print("  Evaluated %i individuals" % len(pop))  # 这时候，pop的长度还是300呢    print("  迭代 %i 次" % NGEN)    t1 = time.clock()    # Begin the evolution      开始进化了哈！！！注意注意注意！就是一个for循环里了！NGEN次--代数    for g in range(NGEN):        if g % 10 == 0:            print("-- Generation %i --" % g)        # Select the next generation individuals        offspring = toolbox.select(pop, len(pop))        # Clone the selected individuals        offspring = list(map(toolbox.clone, offspring))        # Apply crossover and mutation on the offspring        for child1, child2 in zip(offspring[::2], offspring[1::2]):            # cross two individuals with probability CXPB            if random.random() < CXPB:                toolbox.mate(child1, child2)                # fitness values of the children                # must be recalculated later                del child1.fitness.values                del child2.fitness.values        for mutant in offspring:            # mutate an individual with probability MUTPB            if random.random() < MUTPB:                toolbox.mutate(mutant)                del mutant.fitness.values        # Evaluate the individuals with an invalid fitness        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]        fitnesses = map(toolbox.evaluate, invalid_ind)        for ind, fit in zip(invalid_ind, fitnesses):            ind.fitness.values = fit        # print("  Evaluated %i individuals" % len(invalid_ind))        # The population is entirely replaced by the offspring        pop[:] = offspring        # 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))        # print("  Avg %s" % mean)        # print("  Std %s" % std)    print("-- End of (successful) evolution --")    best_ind = tools.selBest(pop, 1)[0]    print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))    print('预测数据')    # PevalOneMax([0.6222847026584997, 0.9952779203368345, 0.10901692485431957, 0.8966275594961192, 0.9692993203252058])    print('该次遗传算法的出的最好的参数的通过数:')    PevalOneMax(best_ind)    print('出题方给的定价规律的预测通过数',TevalOneMax())    t2 = time.clock()    print(t2 - t1)

全部代码

# - * - coding: utf - 8 -*-# 作者：田丰(FontTian)# 创建时间:'2017/9/17'#    This file is part of DEAP.##    DEAP is free software: you can redistribute it and/or modify#    it under the terms of the GNU Lesser General Public License as#    published by the Free Software Foundation, either version 3 of#    the License, or (at your option) any later version.##    DEAP is distributed in the hope that it will be useful,#    but WITHOUT ANY WARRANTY; without even the implied warranty of#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the#    GNU Lesser General Public License for more details.##    You should have received a copy of the GNU Lesser General Public#    License along with DEAP. If not, see <http://www.gnu.org/licenses/>.#    example which maximizes the sum of a list of integers#    each of which can be 0 or 1import randomfrom deap import basefrom deap import creatorfrom deap import toolsimport timeThresholdValue = 28.6670026583creator.create("FitnessMax", base.Fitness, weights=(1.0,))  # 定义最大化适应度creator.create("Individual", list, fitness=creator.FitnessMax)  # 这里的list种群的数据类型toolbox = base.Toolbox()# Attribute generator: define 'attr_bool' to be an attribute ('gene')#                      which corresponds to integers sampled uniformly#                      from the range [0,1] (i.e. 0 or 1 with equal#                      probability)toolbox.register("attr_bool", random.random)  # 包含了0,1的随机整数,初始化种群# Structure initializers: define 'individual' to be an individual#                         consisting of 100 'attr_bool' elements ('genes')toolbox.register("individual", tools.initRepeat, creator.Individual,                 toolbox.attr_bool, 5)# define the population to be a list of 'individual'stoolbox.register("population", tools.initRepeat, list, toolbox.individual)import pandas as pddf2 = pd.read_csv('/home/fonttian/Data/MM2017/db.csv')npones = df2['npones']level = df2['level']length = df2['length']MiDu = df2['MiDu']npjt = df2['npjt']listlen = df2['listlen']price = df2['price']def evalOneMax(individual):    newPrice = (individual[0] - 0.5) * npones * 20 + (individual[1] - 0.5) * length * 20 + (individual[2] - 0.5) * MiDu * 20 + (individual[3] - 0.5) * level * 20 + (individual[4]) * 100 +listlen * 65.7    # npones,nplength,npMiDuList3    w2 = [-0.01633732, 0.83539635, -0.06544261, -0.00280863]    xjb = length * w2[0] + level * w2[1] + MiDu * w2[2] + npjt * w2[3]    xjb = xjb * newPrice    sums = 0    for i in range(len(xjb)):        # yuzhi = 28.6670026583        # if xjb[i] >= yuzhi and newPrice[i] <= price[i] *1.1: # 608        # if xjb[i] >= yuzhi: # 655        # yuzhi = 0.474373718686        if xjb[i] >= ThresholdValue and sum(newPrice) <= 57707.5 * 1.3: # 655            sums += 1        else:            pass    return sums,def PevalOneMax(individual):    print((individual[0] - 0.5) * 20, (individual[1] - 0.5) * 20, (individual[2] - 0.5) * 20, (individual[3] - 0.5) * 20, (individual[4]) * 100)    newPrice = (individual[0] - 0.5) * npones * 20 + (individual[1] - 0.5) * length * 20 + (individual[2] - 0.5) * MiDu * 20 + (individual[3] - 0.5) * level * 20 + (individual[4]) * 100    w2 = [-0.01633732, 0.83539635, -0.06544261, -0.00280863]    xjb = length * w2[0] + level * w2[1] + MiDu * w2[2] + npjt * w2[3]    xjb = xjb * newPrice    sums = 0    for i in range(len(xjb)):        if xjb[i] >= ThresholdValue:            sums += 1        else:            pass    print(sums)    print("新的总价:",sum(newPrice),'旧的总价:',sum(price))    return sums,# ---------------------Operator registration---------------------toolbox.register("evaluate", evalOneMax)toolbox.register("mate", tools.cxTwoPoint)toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)toolbox.register("select", tools.selTournament, tournsize=3)def TevalOneMax():    # 原定价模型的通过率    w2 = [-0.01633732, 0.83539635, -0.06544261, -0.00280863]    xjb = length * w2[0] + level * w2[1] + MiDu * w2[2] + npjt * w2[3]    xjb = xjb * price    sum = 0    for i in range(len(xjb)):        if xjb[i] >= ThresholdValue:            sum += 1        else:            pass    return sum# --------------------- main ---------------------def main():    random.seed(64)    # hash(64)is used    # random.seed方法的作用是给随机数对象一个种子值，用于产生随机序列。    # 对于同一个种子值的输入，之后产生的随机数序列也一样。    # 通常是把时间秒数等变化值作为种子值，达到每次运行产生的随机系列都不一样    # create an initial population of 300 individuals (where    # each individual is a list of integers)    pop = toolbox.population(n=300)  # 定义300个个体的种群    # CXPB  is the probability with which two individuals    #       are crossed    #    # MUTPB is the probability for mutating an individual    #    # NGEN  is the number of generations for which the    #       evolution runs   迭代次数    CXPB, MUTPB, NGEN = 0.5, 0.2, 100    print("Start of evolution")    # Evaluate the entire population    fitnesses = list(map(toolbox.evaluate, pop))    # for ind, fit in zip(pop, fitnesses):    #     ind.fitness.values = fit    print("  Evaluated %i individuals" % len(pop))  # 这时候，pop的长度还是300呢    print("  迭代 %i 次" % NGEN)    t1 = time.clock()    # Begin the evolution      开始进化了哈！！！注意注意注意！就是一个for循环里了！NGEN次--代数    for g in range(NGEN):        if g % 10 == 0:            print("-- Generation %i --" % g)        # Select the next generation individuals        offspring = toolbox.select(pop, len(pop))        # Clone the selected individuals        offspring = list(map(toolbox.clone, offspring))        # Apply crossover and mutation on the offspring        for child1, child2 in zip(offspring[::2], offspring[1::2]):            # cross two individuals with probability CXPB            if random.random() < CXPB:                toolbox.mate(child1, child2)                # fitness values of the children                # must be recalculated later                del child1.fitness.values                del child2.fitness.values        for mutant in offspring:            # mutate an individual with probability MUTPB            if random.random() < MUTPB:                toolbox.mutate(mutant)                del mutant.fitness.values        # Evaluate the individuals with an invalid fitness        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]        fitnesses = map(toolbox.evaluate, invalid_ind)        for ind, fit in zip(invalid_ind, fitnesses):            ind.fitness.values = fit        # print("  Evaluated %i individuals" % len(invalid_ind))        # The population is entirely replaced by the offspring        pop[:] = offspring        # 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))        # print("  Avg %s" % mean)        # print("  Std %s" % std)    print("-- End of (successful) evolution --")    best_ind = tools.selBest(pop, 1)[0]    print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))    print('预测数据')    # PevalOneMax([0.6222847026584997, 0.9952779203368345, 0.10901692485431957, 0.8966275594961192, 0.9692993203252058])    print('该次遗传算法的出的最好的参数的通过数:')    PevalOneMax(best_ind)    print('出题方给的定价规律的预测通过数',TevalOneMax())    t2 = time.clock()    print(t2 - t1)if __name__ == "__main__":    # t1 = time.clock()    main()    # t2 = time.clock()    # print(t2-t1)

下载数据文件

链接: https://pan.baidu.com/s/1gfcTzpx 密码: dibq

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