lmfit minimizer不接受scipy minimizer关键字参数

Question

我正试图用lmfit使一些模型适合我的数据。见以下MWE：

import lmfit
import numpy as np

def lm(params, x):
    slope = params['slope']
    interc = params['interc']

    return interc + slope * x

def lm_min(params, x, data):
    y = lm(params, x)
    return data - y

x = np.linspace(0,100,1000)
y = lm({'slope':1, 'interc':0.5}, x)

ydata = y + np.random.randn(1000)

params = lmfit.Parameters()
params.add('slope', 2)
params.add('interc', 1)

fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata), fit_kws={'xatol':0.01})
fit = fitter.minimize(method='nelder')

为了早点完成（准确度现在不是最重要的），我想改变停止拟合的标准。基于docs和SO上的一些搜索，我尝试给出一些关键字参数（fit_kws在下面的行中），这些参数将传递给所使用的最小值。我还尝试使用kws和**{'xatol':0.01}。除此之外，我还在最后一行中尝试了前面提到的选项，在这里我调用fitter.minimize()。但是，在所有情况下，我都会得到一个TypeError，表示它得到了意外的关键字参数：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/STACK/WUR/PhD/Experiments/Microclimate experiment/Scripts/Fluctuations/mwe.py in <module>()
     25 
     26 fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata), fit_kws={'xatol':0.01})
---> 27 fit = fitter.minimize(method='nelder')
     28 

~/anaconda3/envs/py/lib/python3.6/site-packages/lmfit/minimizer.py in minimize(self, method, params, **kws)
   1924                         val.lower().startswith(user_method)):
   1925                     kwargs['method'] = val
-> 1926         return function(**kwargs)
   1927 
   1928 

~/anaconda3/envs/py/lib/python3.6/site-packages/lmfit/minimizer.py in scalar_minimize(self, method, params, **kws)
    906         else:
    907             try:
--> 908                 ret = scipy_minimize(self.penalty, variables, **fmin_kws)
    909             except AbortFitException:
    910                 pass

TypeError: minimize() got an unexpected keyword argument 'fit_kws'

有人知道我如何为特定的解算器添加关键字参数吗？你知道吗

版本信息：

python:3.6.9
scipy:1.3.1
最小拟合：0.9.12

Answer 1

将关键字参数传递给底层scipy解算器的最佳方法就是使用

# Note: valid but will not do what you want
fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata), xatol=0.01)
fit = fitter.minimize(method='nelder')

或者

# Also: valid but will not do what you want
fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata))
fit = fitter.minimize(method='nelder', xatol=0.01)

这里的主要问题是xatol不是底层解算器scipy.optimize.minimize()的有效关键字参数。相反，您可能想使用tol：

fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata), tol=0.01)
fit = fitter.minimize(method='nelder')

或者

fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata))
fit = fitter.minimize(method='nelder', tol=0.01)

Answer 2

在github issue中，我找到了以下解决方案：

fit = fitter.minimize(method='nelder', **{'options':{'xatol':4e-4}})

更新
正如@dashesy所提到的，这与写作是一样的：

fit = fitter.minimize(method='nelder', options={'xatol':4e-4})

这也适用于其他解算器选项。你知道吗