为什么我从scipy.optimize.fmin得到不正确的结果?

问题描述

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

为什么我会得到r = -69，-70和-71?根据这些数据，我应该得到r = 0.333、0.555和0.999.

Why am I getting r = -69, -70, and -71?I should be getting r = 0.333, 0.555, and 0.999 with this data.

推荐答案

scipy.optimize.fmin会将尝试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

scipy.optimize.fmin will pass the value it is trying to minimize as the first argument to the function. If you rewrite your function as

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

您得到正确的结果

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.]

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