"""MyProblem.py"""
import numpy as np
import geatpy as ea
from sklearn import preprocessing
from sklearn.model_selection import cross_val_score
from multiprocessing.dummy import Pool as ThreadPool
from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn import model_selection
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error#平均绝对误差，越小越好
from sklearn.metrics import explained_variance_score#解释方差分，越近1越好
from sklearn.metrics import mean_absolute_error#中位数绝对误差
from sklearn.metrics import r2_score#越近1，解释程度越高
class MyProblem(ea.Problem): # 继承Problem父类
def __init__(self):
name = 'MyProblem' # 初始化name（函数名称，可以随意设置）
M =1 # 初始化M（目标维数）
maxormins = [-1] # 初始化maxormins（目标最小最大化标记列表，1：最小化该目标；-1：最大化该目标）
Dim = 32 # 初始化Dim（决策变量维数）
varTypes = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] # 初始化varTypes（决策变量的类型，元素为0表示对应的变量是连续的；1表示是离散的）
lb = [1.835,2.55,9.11,0.17,2.028,0.0198,6.0,9.89111,967.684464,5.2597,4.2183,2.52261,3.6183,2.354,2.413,0.433786,0.542036,0.480721,2.245,1.015,2.7709,0.351,0.0,0.418,255.8193102,0.0,0.0,0.0,1.68802,2.55,1.6811,2.55]# 决策变量下界
ub = [ 2.0167,2.676666667,10.5,0.368,2.19,0.13068,7.0,10.6516,1208.4941,10.7293,6.3184,3.133,3.863,2.63018,2.653,0.729645,0.681679,0.628998,2.834,2.164,3.437,0.66,0.41,0.761,374.7309258,34.13703714,34.51384964,73.42387428,2.062256,2.96,1.84,2.87163]# 决策变量上界
lbin =[ 2.0167,2.676666667,10.5,0.368,2.19,0.13068,7.0,10.6516,1208.4941,10.7293,6.3184,3.133,3.863,2.63018,2.653,0.729645,0.681679,0.628998,2.834,2.164,3.437,0.66,0.41,0.761,374.7309258,34.13703714,34.51384964,73.42387428,2.062256,2.96,1.84,2.87163] # 决策变量下边界（0表示不包含该变量的下边界，1表示包含）
ubin = [ 2.0167,2.676666667,10.5,0.368,2.19,0.13068,7.0,10.6516,1208.4941,10.7293,6.3184,3.133,3.863,2.63018,2.653,0.729645,0.681679,0.628998,2.834,2.164,3.437,0.66,0.41,0.761,374.7309258,34.13703714,34.51384964,73.42387428,2.062256,2.96,1.84,2.87163] # 决策变量上边界（0表示不包含该变量的上边界，1表示包含）
# 调用父类构造方法完成实例化
ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
# 目标函数计算中用到的一些数据
df=pd.read_excel('traindata-0.5D0 - he.xlsx')
data_targets = []
data=df.ix[0:,4:36].values#样本
data=data.tolist()
target_names=df.ix[0:,36].values#类
self.data=data
self.target_names=target_names
def aimFunc(self, pop): # 目标函数
Vars = pop.Phen # 得到决策变量矩阵
pop.ObjV = np.zeros((pop.sizes, 1)) # 初始化种群个体目标函数值列向量
def subAimFunc(i):
from sklearn.model_selection import train_test_split
Xtrain, Xtest, Ytrain, Ytest = train_test_split(self.data,self.target_names,test_size=0.2,random_state=10)
model= RandomForestRegressor(n_estimators=1,random_state=10)
model.fit(Xtrain, Ytrain)
pred=model.predict(Vars)
scores=mean_squared_error(Ytest,pred)
pool=ThreadPool(2)
pool.map(subAimFunc, list(range(pop.sizes)))
pop.ObjV[i] = len(np.where(pop.Phen-pred))
import geatpy as ea
import numpy as np
from MyPromblem import MyProblem
from sklearn.metrics import mean_absolute_error#中位数绝对误差
from sklearn.metrics import mean_squared_error#平均绝对误差，越小越好
from sklearn.metrics import r2_score#越近1，解释程度越高
from sklearn.metrics import explained_variance_score#解释方差分，越近1越好
"""================================实例化问题对象============================="""
problem = MyProblem() # 生成问题对象
"""==================================种群设置================================"""
Encoding = 'RI' # 编码方式
NIND =50 # 种群规模
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges, problem.borders) # 创建区域描述器
population = ea.Population(Encoding, Field, NIND) # 实例化种群对象（此时种群还没被初始化，仅仅是完成种群对象的实例化）
"""================================算法参数设置==============================="""
myAlgorithm = ea.soea_GGAP_SGA_templet(problem, population) # 实例化一个算法模板对象
myAlgorithm.MAXGEN = 300 # 最大进化代数
myAlgorithm.trappedValue = 1e-6 # “进化停滞”判断阈值
myAlgorithm.maxTrappedCount = 100 # 进化停滞计数器最大上限值，如果连续maxTrappedCount代被判定进化陷入停滞，则终止进化
"""===========================调用算法模板进行种群进化===========================
调用run执行算法模板。NDSet是一个种群类Population的对象。
NDSet.ObjV为最优解个体的目标函数值；NDSet.Phen为对应的决策变量值。
详见Population.py中关于种群类的定义。
"""
myAlgorithm.drawing=2
[population, obj_trace, var_trace] = myAlgorithm.run() # 执行算法模板
Chrom = ea.crtbp(NIND,pop.Lind)
population.save()
print(problem.getReferObjV())
best_gen=np.argmin(problem.maxormins*obj_trace[:,1])
best_ObjV = obj_trace[best_gen, 1]
print('最优的目标函数值为：%s'%(best_ObjV))
print('最优的控制变量值为：')
for i in range(var_trace.shape[1]):
print('有效进化代数：%s'%(obj_trace.shape[0]))
print('最优的一代是第 %s 代'%(best_gen + 1))
print('评价次数：%s'%(myAlgorithm.evalsNum))
print('时间已过 %s 秒'%(myAlgorithm.passTime))
代码运行以后就是这样一根线
请问这样的问题要如何解决
