通过 lambda 调用函数时维度错误

如何解决通过 lambda 调用函数时维度错误

我对以下代码片段有问题，我在其中优化了一个函数（最小化波动）。

from scipy import optimize as sco
import numpy as np

def risk_measure(covMatrix,weights):
    risk = np.dot(weights,np.dot(covMatrix,weights))
    return risk

prescribed_esg = 6 # esg score between 0 and 10 used as threshold in the esg_constraint


# Covariance and return matrix
V = np.matrix([[84.76695659,20.8854772,20.62182415,74.73652696,14.35995947],[20.8854772,35.22429277,12.95439707,32.22912903,12.96449085],[20.62182415,44.02079739,38.73627316,9.46608475],[74.73652696,178.86640813,33.40281336],[14.35995947,12.96449085,9.46608475,33.40281336,32.38514103]])
R = np.matrix([[-0.32264539,-0.08469428,1.27628749,-0.23207085,0.21012106]]).T
# Mean ESG score of each company
esgarr = np.matrix([[8.24336898,4.6373262,8.30657754,4.65406417,3.43620321]]).T

# Bounds and constraints
N = len(R) # number of instruments
bounds = ((-10,10),)*N # allow shorting,bounds of stocks
constraints = {'type': 'eq','fun': lambda weights: weights.sum() - 1}
esg_constraint = {'type': 'eq','fun': lambda weights: np.dot(weights,esgarr) - prescribed_esg}

esgmvp = sco.minimize(lambda x: risk_measure(V,x),# function to be minimized
                      N * [1 / N],# initial guess
                      bounds=bounds,# boundary conditions
                      constraints =[constraints,esg_constraint],# equality constraints)
                     ) 

esgmvp_weights = list(esgmvp['x'])
esgmvp_risk = esgmvp['fun']
esgmvp_esg = np.dot(esgmvp_weights,esgarr)

带有错误信息

<ipython-input-252-0d6bf5d30ccf> in risk_measure(covMatrix,weights)
      3 
      4 def risk_measure(covMatrix,weights):
----> 5     risk = np.dot(weights,weights))
      6     return risk
      7 

<__array_function__ internals> in dot(*args,**kwargs)

ValueError: shapes (5,) and (1,5) not aligned: 5 (dim 0) != 1 (dim 0)

如果我创建一个独立的权重矩阵，我就能得到结果

weights = np.matrix([[1,1,1]])
risk = np.dot(weights,np.dot(V,weights.T))

但这在我原来的函数中转置时不起作用。

解决方法

以下解决了它

V = np.squeeze(np.asarray(V))
esg_constraint = {'type': 'eq','fun': lambda weights: np.dot(weights,esgarr).sum() - prescribed_esg}

我也编辑了函数

def risk_measure(covMatrix,weights):
    risk = np.dot(weights.T,np.dot(covMatrix,weights))
    return risk