python - 为什么我从 scipy.optimize.fmin 得到不正确的结果？

Question

import pandas as pd
from scipy.optimize import fmin

data = pd.DataFrame({'DIV': [1,2,3]*3,
                     'MONTH': ['May','May','May','June','June','Jun','Jul','Jul','Jul'],
                     'C':[8]*9,
                     'U':[3,2,1]*3,
                     'S':[9]*9})

data.to_csv(r'C:\Users\mbabski\Documents\Unit Plan Summer 2016\data_test.csv')

def return_array(x):
    return x.values

def mape(c,u,s,r): #returns an array of line level Mean Absolute Percentage Errors
    p = c + u * r
    m = abs(1.0-(p/s))
    return m

def e(c,u,s,r): #calculates average of the MAPEs
    return np.mean(mape(c,u,s,r))

for d in range(1,4):
    div_data = data[data.DIV==d]
    c = return_array(div_data.C)
    u = return_array(div_data.U)
    s = return_array(div_data.S)
    r0 = [[1.0]]
    t = fmin(e,r0,args=(c,u,s))
    print 'r:',t

Optimization terminated successfully.
Current function value: 0.000000
Iterations: 29
Function evaluations: 58
r: [-69.]
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 29
Function evaluations: 58
r: [-70.]
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 29
Function evaluations: 58
r: [-71.]

为什么我得到 r = -69、-70 和 -71？使用此数据我应该得到 r = 0.333、0.555 和 0.999。

Answer 1

scipy.optimize.fmin 会将它试图最小化的值作为函数的第一个参数传递。如果您将函数重写为

def e(r,c,u,s): #calculates average of the MAPEs
    return np.mean(mape(c,u,s,r))

你得到了正确的结果

for d in range(1,4):
    div_data = data[data.DIV==d]
    c = return_array(div_data.C)
    u = return_array(div_data.U)
    s = return_array(div_data.S)
    r0 = [[1.0]]
    t = fmin(e,r0,args=(c,u,s))
    print 'r:',t

Optimization terminated successfully.
         Current function value: 0.000011
         Iterations: 16
         Function evaluations: 32
r: [ 0.33330078]
Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 15
         Function evaluations: 30
r: [ 0.5]
Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 10
         Function evaluations: 20
r: [ 1.]