Scipy.optimize：最小化函数产生错误

Question

我正在尝试使用 优化预测参数scipy.optimize。我按照教程进行操作，还在 stackoverflow 上找到了一些很好的示例，但我遇到了一个我无法解决的问题。我开始怀疑使用 pandas 是否是 scipy 的糟糕选择？

我已将我的代码设置如下：

import simpy

import numpy as np

import matplotlib.pyplot as plt

from scipy.stats import norm

import pandas as pd

import statistics as stat

import math as m

#from sklearn.grid_search import ParameterGrid

from scipy.optimize import minimize

###dataframe for the simulation

df = pd.read_csv('simulation_df_data_2018_2.csv')

with pd.option_context("max_rows", None,"max_columns", None):

    print(df.head())

for i in df.index:

    alpha = 0.2

    beta = 0.3

    

    x = np.array([alpha, beta])

 

    def holts(x):

        LO = np.int(df['average_demand'].loc[i])

        print(type(LO))

        TO = ((df['m2'].loc[i] - df['m3'].loc[i]) + (df['m1'].loc[i] - df['m2'].loc[i])) / 2

        L1 = round(x[0] * df['m3'].loc[i] + (1 - x[0]) * (

                LO + TO))

        T1 = x[1] * (L1 - LO) + (1 - x[1]) * TO

        L2 = round(x[0] * df['m2'].loc[i] + (1 - x[0]) * (

                L1 + T1))

        T2 = x[1] * (L2 - L1) + (1 - x[1]) * T1

        L3 = round(x[0] * df['m1'].loc[i] + (1 - x[0]) * (

                L2 + T2))

        T3 = beta * (L3 - L2) + (1 - beta) * T2

        LT1 = round(L3 + 1 * T3)

        MSE = ((df['m3'].loc[i] - L1) + (df['m2'].loc[i] - L2) + (

                df['m2'].loc[i] - L3)) ** 2 / 3

        return MSE

    #print(holts(x))

    x0 = [0.1,0.1]

    result = minimize(holts, x0, bounds=[(0,1),(0,1)], method="SLSQP")

    print(result)

    print(x)

Answer 1

因为我不知道你的代码到底在做什么，所以我无法判断下面的代码是否正确。但只是为了指导如何使用scipy.optimize.minimize函数，它应该类似于以下内容：import numpy as npimport pandas as pdfrom scipy.optimize import minimizedf = pd.read_csv('simulation_df_data_2018_2.csv')for i in df.index:    # Since you said "alpha" and "beta" are the variable of "holts" function    # I changed all "alpha" and "beta" to "x[0]" and "x[1]" respectively.    def holts(x):        LO = np.int(df['average_demand'].loc[i])        TO = ((df['m2'].loc[i] - df['m3'].loc[i]) + (df['m1'].loc[i] - df['m2'].loc[i])) / 2        L1 = x[0] * df['m3'].loc[i] + (1 - x[0]) * (LO + TO)        T1 = x[1] * (L1 - LO) + (1 - x[1]) * TO        L2 = x[0] * df['m2'].loc[i] + (1 - x[0]) * (L1 + T1)        T2 = x[1] * (L2 - L1) + (1 - x[1]) * T1        L3 = x[0] * df['m1'].loc[i] + (1 - x[0]) * (L2 + T2)        MSE = ((df['m3'].loc[i] - L1) + (df['m2'].loc[i] - L2) + (df['m2'].loc[i] - L3)) ** 2 / 3        return MSE    x0 = np.array([0.1, 0.1])  # initial guess for the points "x" which "holts(x)" attains its minimum.    result = minimize(holts, x0, bounds=[(0, 1), (0, 1)], method="SLSQP", options={'disp': True})    print(result)    print()哪个打印Optimization terminated successfully    (Exit mode 0)            Current function value: 16.921875002444228            Iterations: 5            Function evaluations: 15            Gradient evaluations: 5     fun: 16.921875002444228     jac: array([ 8.06808472e-04, -6.23427415e+00]) message: 'Optimization terminated successfully'    nfev: 15     nit: 5    njev: 5  status: 0 success: True       x: array([0.75000606, 1.        ])Optimization terminated successfully    (Exit mode 0)            Current function value: 3.0792324286388828e-09            Iterations: 7            Function evaluations: 24            Gradient evaluations: 7     fun: 3.0792324286388828e-09     jac: array([-0.00833083, -0.00081578]) message: 'Optimization terminated successfully'    nfev: 24     nit: 7    njev: 7  status: 0 success: True       x: array([0.1495232 , 0.25665903])Optimization terminated successfully    (Exit mode 0)            Current function value: 1.9951658230345873e-10            Iterations: 9            Function evaluations: 32            Gradient evaluations: 9     fun: 1.9951658230345873e-10     jac: array([0.03791677, 0.00858221]) message: 'Optimization terminated successfully'    nfev: 32     nit: 9    njev: 9  status: 0 success: True       x: array([0.37155192, 0.19218713])Optimization terminated successfully    (Exit mode 0)            Current function value: 114.53090716252409            Iterations: 11            Function evaluations: 37            Gradient evaluations: 11     fun: 114.53090716252409     jac: array([ 3.06129456e-04, -3.04457455e+01]) message: 'Optimization terminated successfully'    nfev: 37     nit: 11    njev: 11  status: 0 success: True       x: array([0.7171298, 1.       ])Optimization terminated successfully    (Exit mode 0)            Current function value: 33.333333333333336            Iterations: 2            Function evaluations: 6            Gradient evaluations: 2     fun: 33.333333333333336     jac: array([-403.33331966,   -0.        ]) message: 'Optimization terminated successfully'    nfev: 6     nit: 2    njev: 2  status: 0 success: True       x: array([1., 1.])更新：似乎函数是函数不起作用的round原因，因为函数会在函数图上创建许多高原。我删除了功能并更新了打印的结果。minimizeroundholtsround