问题描述
我有两个数据框(df_1,df_2):
# Index
df_1 = df_1.set_index('O')
df_1_M = df_1.M
df_1_M = df_1_M.sort_index()
# Fun
def fun(z,*params):
A,B,C = z
# score
df_2['S'] = df_2['X']*A + df_2['Y']*B + df_2['Z']*C
# Top score
df_Sort = df_2.sort_values(['S','X','M'],ascending=[False,True,True])
df_O = df_Sort.set_index('O')
M_Top = df_O[~df_O.index.duplicated(keep='first')].M
M_Top = M_Top.sort_index()
# Compare the top scoring row for each O to df_1
df_1_R = df_1_M.reindex(M_Top.index) # Nan
T_N_T = M_Top == df_1_R
# Record the results for the given values of A,C
df_Res = pd.DataFrame({'it_is':T_N_T}) # is this row of df_1 the same as this row of M_Top?
# p_hat = TP / (TP + FP)
p_hat = df_Res.sum() / len(df_Res.index)
return -p_hat
from scipy.optimize import brute
# Range
min_ = -2
max_ = 2
step = .5
ran_ge = slice(min_,max_+step,step)
ranges = (ran_ge,ran_ge,ran_ge)
# Params
params = (df_1,df_2)
# Brute
resbrute = brute(fun,ranges,args=params,full_output=True,finish=None)
print('Global maximum ',resbrute[0])
print('Function value at global maximum ',-resbrute[1])
我可以使用蛮力对其进行优化:
Global maximum [-2. 0.5 1.5]
Function value at global maximum 0.6666666666666666
哪个给:
from scipy.optimize import differential_evolution
# Bounds
min_ = -2
max_ = 2
ran_ge = (min_,max_)
bounds = [ran_ge,ran_ge]
# Params
params = (df_1,df_2)
# DE
DE = differential_evolution(fun,bounds,args=params)
但是,当尺寸和分辨率提高时,这会花费太长时间。为了节省时间,我想通过差分进化(DE)对其进行优化。我尝试过:
ValueError: The truth value of a Series is ambiguous. Use a.empty,a.bool(),a.item(),a.any() or a.all().
但是我得到了
Arrdata = [{fdata:[],title:"1","desc":""},{fdata:[],title:"2","desc":""}]
请问有什么想法为什么要用蛮力而不是通过差异进化吗?如何通过差分进化使其发挥作用?
解决方法
看代码,fun(z,*params)
函数返回一个序列,而differential_evolution
不知道如何处理它。
# pandas.core.series.Series
type(p_hat)
将fun(z,*params)
的返回值更改为:
return -p_hat[0]
我们得到正确的答案:
# Function value at global maximum 0.6666666666666666
print('Function value at global maximum ',-DE.fun)
代码修复:
import pandas as pd
df_1 = pd.DataFrame({'O' : [1,2,3],'M' : [2,8,3]})
df_2 = pd.DataFrame({'O' : [1,1,3,'M' : [9,4,6,7,5,4],'X' : [2,9],'Y' : [3,7],'Z' : [2,1]})
# Index
df_1 = df_1.set_index('O')
df_1_M = df_1.M
df_1_M = df_1_M.sort_index()
# Fun
def fun(z,*params):
A,B,C = z
# Score
df_2['S'] = df_2['X'] * A + df_2['Y'] * B + df_2['Z'] * C
# Top score
df_Sort = df_2.sort_values(['S','X','M'],ascending=[False,True,True])
df_O = df_Sort.set_index('O')
M_Top = df_O[~df_O.index.duplicated(keep='first')].M
M_Top = M_Top.sort_index()
# Compare the top scoring row for each O to df_1
df_1_R = df_1_M.reindex(M_Top.index) # Nan
T_N_T = M_Top == df_1_R
# Record the results for the given values of A,C
df_Res = pd.DataFrame({'it_is': T_N_T}) # is this row of df_1 the same as this row of M_Top?
# p_hat = TP / (TP + FP)
p_hat = df_Res.sum() / len(df_Res.index)
return -p_hat[0]
from scipy.optimize import differential_evolution
# Bounds
min_ = -2
max_ = 2
ran_ge = (min_,max_)
bounds = [ran_ge,ran_ge,ran_ge]
# Params
params = (df_1,df_2)
# DE
DE = differential_evolution(fun,bounds,args=params)
print('Function value at global maximum ',-DE.fun)