deap實戰_2017中國數學建模大賽_B題_第二題
阿新 • • 發佈：2018-12-31
簡介
補充:本例子僅僅是之前deap類庫的一個實戰例子,所以先別問我數學建模的事情,我暫時不想回答(還有為毛踩我文章…..我本來就不是寫數學建模的……╮(╯▽╰)╭)(2017/10/31)
原問題是給出一個定價策略,證明其相較於原來定價策略的優點.
那麼首先我們第一題第二問得到了一個 價格-完成率 函式,此時我們需要的是給出一個新的定價函式,並利用遺傳演算法得到最佳引數.
思路
- 編碼–>我們需要編碼的是定價函式的引數
- 評價函式—->將編碼輸入的定價函式得到價格,然後將價格輸入之前得到的 價格-完成率 函式得到完成率
- 求解的目標應當是最大化完成率
- 為了控制成本需要對價格進行一定的限制,避免為了提高完成率,而過高定價
Types
import random
from deap import base
from deap import creator
from deap import tools
import time
ThresholdValue = 28.6670026583
creator.create("FitnessMax", base.Fitness, weights=(1.0,)) # 定義最大化適應度
creator.create("Individual", list, fitness=creator.FitnessMax) # 這裡的list種群的資料型別
toolbox = base.Toolbox ReadData
讀取資料,為之後的計算做準備
import pandas as pd
df2 = 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']
定義評價函式(適應度函式)
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, # 注意最後的 , 該檔案中必須要有,不然會報錯註冊其他引數(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)設計執行程式,獲取我們想要的結果
def main():
random.seed(64)
# hash(64)is used
# random.seed方法的作用是給隨機數物件一個種子值，用於產生隨機序列。
# 對於同一個種子值的輸入，之後產生的隨機數序列也一樣。
# 通常是把時間秒數等變化值作為種子值，達到每次執行產生的隨機系列都不一樣
# create an initial population of 300 individuals (where
# each individual is a list of integers)
pop = toolbox.population(n=300) # 定義300個個體的種群
# 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 迭代次數
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)) # 這時候，pop的長度還是300呢
print(" 迭代 %i 次" % NGEN)
t1 = time.clock()
# Begin the evolution 開始進化了哈！！！注意注意注意！就是一個for迴圈裡了！NGEN次--代數
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('預測資料')
# PevalOneMax([0.6222847026584997, 0.9952779203368345, 0.10901692485431957, 0.8966275594961192, 0.9692993203252058])
print('該次遺傳演算法的出的最好的引數的通過數:')
PevalOneMax(best_ind)
print('出題方給的定價規律的預測通過數',TevalOneMax())
t2 = time.clock()
print(t2 - t1)全部程式碼
# - * - coding: utf - 8 -*-
# 作者：田豐(FontTian)
# 建立時間:'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 1
import random
from deap import base
from deap import creator
from deap import tools
import time
ThresholdValue = 28.6670026583
creator.create("FitnessMax", base.Fitness, weights=(1.0,)) # 定義最大化適應度
creator.create("Individual", list, fitness=creator.FitnessMax) # 這裡的list種群的資料型別
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的隨機整數,初始化種群
# 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's
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
import pandas as pd
df2 = 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("新的總價:",sum(newPrice),'舊的總價:',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():
# 原定價模型的通過率
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方法的作用是給隨機數物件一個種子值，用於產生隨機序列。
# 對於同一個種子值的輸入，之後產生的隨機數序列也一樣。
# 通常是把時間秒數等變化值作為種子值，達到每次執行產生的隨機系列都不一樣
# create an initial population of 300 individuals (where
# each individual is a list of integers)
pop = toolbox.population(n=300) # 定義300個個體的種群
# 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 迭代次數
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)) # 這時候，pop的長度還是300呢
print(" 迭代 %i 次" % NGEN)
t1 = time.clock()
# Begin the evolution 開始進化了哈！！！注意注意注意！就是一個for迴圈裡了！NGEN次--代數
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('預測資料')
# PevalOneMax([0.6222847026584997, 0.9952779203368345, 0.10901692485431957, 0.8966275594961192, 0.9692993203252058])
print('該次遺傳演算法的出的最好的引數的通過數:')
PevalOneMax(best_ind)
print('出題方給的定價規律的預測通過數',TevalOneMax())
t2 = time.clock()
print(t2 - t1)
if __name__ == "__main__":
# t1 = time.clock()
main()
# t2 = time.clock()
# print(t2-t1)