scipy.optimize.leastsq 和 scipy.optimize.least_squares 之间的区别？

Question

I've been running an optimization process using the legacy scipy.optimize.leastsq Now I want to switch to scipy.optimize.least_squares (I need to introduce bounds). But least_squares throws an error which I can't debug. Below my code, I am doing exactly the same with least_squares as with leastsq .

import scipy
from scipy.optimize import leastsq, least_squares
print(scipy.__version__)

def residuals_cmrset_as_2009JoH(x0, df):
    k_max= x0[0]
    a= x0[1]
    alpha= x0[2]
    b= x0[3]
    beta= x0[4]
    k_Ei_max= x0[5]
    k_CMI= x0[6]
    C_CMI= x0[7]
    CMI_max= x0[8]
    EVI_min= x0[9]
    EVI_max= x0[10]

    df['aet_cmrset'] = aet_cmrset_as_2009JoH(df.evi, df.gvmi, df.pet, df.rain, 
                            k_max, a, alpha, b, beta, k_Ei_max, k_CMI, C_CMI, CMI_max, EVI_min, EVI_max) 
    return(df.aet_cmrset - df.AET_observed)
    

print('run calibration with leastsq')
x, flag = leastsq(residuals_cmrset_as_2009JoH, 
                  np.transpose(x0), 
                  args=(df_calibration))
print('this is the result from leastsq')
print(x)

print('run calibration with least_squares')
x, flag = least_squares(residuals_cmrset_as_2009JoH, 
                        np.transpose(x0), 
                        args=(df_calibration)) 
print('this is the result from least_squares')
print(x)

and this is the output:

1.2.0
run calibration with leastsq
this is the result from leastsq
[ 0.99119625  1.44145154  1.12799561 27.41023799  2.60102797  0.09771226
  1.14979708 -0.24298292  1.          0.          0.9       ]
run calibration with least_squares
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-16-bc305703822b> in <module>
     30 x, flag = least_squares(residuals_cmrset_as_2009JoH, 
     31                         np.transpose(x0),
---> 32                         args=(df_calibration)) 
     33 print('this is the result from least_squares')
     34 print(x)

/apps/python/3.7.2/lib/python3.7/site-packages/scipy-1.2.0-py3.7-linux-x86_64.egg/scipy/optimize/_lsq/least_squares.py in least_squares(fun, x0, jac, bounds, method, ftol, xtol, gtol, x_scale, loss, f_scale, diff_step, tr_solver, tr_options, jac_sparsity, max_nfev, verbose, args, kwargs)
    796         x0 = make_strictly_feasible(x0, lb, ub)
    797 
--> 798     f0 = fun_wrapped(x0)
    799 
    800     if f0.ndim != 1:

/apps/python/3.7.2/lib/python3.7/site-packages/scipy-1.2.0-py3.7-linux-x86_64.egg/scipy/optimize/_lsq/least_squares.py in fun_wrapped(x)
    791 
    792     def fun_wrapped(x):
--> 793         return np.atleast_1d(fun(x, *args, **kwargs))
    794 
    795     if method == 'trf':

TypeError: residuals_cmrset_as_2009JoH() takes 2 positional arguments but 11 were given

Any help will be welcome

Answer 1

Both functions specify that args is supposed to be a tuple. But leastsq has, near the start this

if not isinstance(args, tuple):
    args = (args,)

I don't see something equivalent in least_squares . That step "protects" leastsq in case the user makes a mistake and passes an array instead of the specified tuple.