Scipy.optimize 最小化函数参数不适用于多个向量输入

Question

我正在尝试为某些（收益率曲线）市场数据生成 PCA 权重的解决方案。但是，我在 scipy.optimize.minimize 函数中收到一条错误消息。

主要的错误是它似乎在将参数读入最小化函数时出错(error_sum)。

我在这里查找了通用形式，但是当我使用它时它对我的代码不起作用。

import scipy as sc
import scipy.optimize as optimize
from scipy.optimize import minimize

w1 = 1.0
w2 = 1.0
w3 = 1.0

row_C = np.zeros(len(df_.columns)) # initialize current row as zero
row_T = df_.iloc[-1].values # get the target row, which we have set as the last row of the panda dataframe

row_c = np.array([-0.35865725, 0.52793819, 0.70654759, -0.28909144, 1.08467752, 0.91287324])
row_t = np.array([1.7971, 2.5756, 2.2005, 1.4966, 1.45  , 1.8022])

def error_sum(row_c, row_t, params): # row_c is estimated and row_t is target
    w1 = params[0]
    w2 = params[1]
    w2 = params[2]

    if len(row_c) != len(row_t): return print('error where x and y points are not same length')
    for cnt in range(len(row_c)):
        row_c[cnt] = w1 * row1[cnt] + w2 * row2[cnt] + w3 * row3[cnt]

    return np.sum(np.abs(row_c - row_t))

for cnt in range(len(df_.columns)): # loop to calculate the PCA-based moves
    row_c[cnt] = w1 * row1[cnt] + w2 * row2[cnt] + w3 * row3[cnt]

print(np.sum(np.abs(row_c - row_t))) # this is to get the sum of absolute difference errors
print(error_sum(row_c, row_t, x0))

x0 = np.array([1.0, 1.0, 1.0]) # parameters to optimize
bnds = ((-10.0, 10.0), (-10.0, 10.0), (-10.0, 10.0)) # boundary conditions of x0 parameter set
options = {'maxiter': 100}

res = minimize(error_sum, x0 ,(row_c, row_t), bounds = bnds, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})

报错信息如下

error where x and y points are not same length

TypeError                                 Traceback (most recent call last)
<ipython-input-158-8c50b421e58a> in <module>()
     32 options = {'maxiter': 100}
     33 
---> 34 res = minimize(error_sum, x0 ,(row_c, row_t), bounds = bnds, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})

C:\ProgramData\Anaconda3\lib\site-packages\scipy\optimize\_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    473                       callback=callback, **options)
    474     elif meth == 'nelder-mead':
--> 475         return _minimize_neldermead(fun, x0, args, callback, **options)
    476     elif meth == 'powell':
    477         return _minimize_powell(fun, x0, args, callback, **options)

C:\ProgramData\Anaconda3\lib\site-packages\scipy\optimize\optimize.py in _minimize_neldermead(func, x0, args, callback, maxiter, maxfev, disp, return_all, initial_simplex, xatol, fatol, **unknown_options)
    549         doshrink = 0
    550 
--> 551         if fxr < fsim[0]:
    552             xe = (1 + rho * chi) * xbar - rho * chi * sim[-1]
    553             fxe = func(xe)

TypeError: '>' not supported between instances of 'float' and 'NoneType'

Answer 1

尝试将 error_sum 定义中参数的顺序更改为

def error_sum(params, row_c, row_t)

如果你想获得最佳的参数并像这样调用函数：

minimize(error_sum, x0, args=(row_c, row_t), bounds = bnds, method='nelder-mead', options={'xtol': 1e-8, 'disp': True})

Scipy.optimize 最小化函数参数不适用于多个向量输入

Scipy.optimize minimize function arguments don't work for multiple vector inputs

mathematical-optimization

scipy

python-3.x

scipy-optimize-minimize