Question

我在Canopy中使用python 2.7，并且我试图通过最小化数据和模型预测之间的均方误差来拟合模型的6个参数。我使用COBYLA，因为我需要参数值的界限，而且我没有梯度。

目前，我有：

import numpy as np
import scipy.optimize as opt

def cost_func(pars,y,x):
    y_hat = model_output(pars,x)
    mse = np.mean((y-y_hat)**2)
    return mse

def make_constraints(par_min,par_max):
    cons = []    
    for (i,(a,b)) in enumerate(zip(par_min,par_max)):
        lower = lambda x: x[i] - a
        upper = lambda x: b - x[i]
        cons = cons + [lower] + [upper]
    return cons

def estimate_parameters(par_min, par_max,par_init,x,y):
    cons = make_constraints(par_min,par_max)
    opt_pars = opt.fmin_cobyla(cost_func,pars,cons,args=([y,x]))
    return opt_pars

然而我收到错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-63-9e84e10303e1> in <module>()
----> 1 opt_pars = estimate_parameters(par_min,par_max,par_init,x,y)

<ipython-input-61-f38615d82ee5> in estimate_parameters(par_min,par_max,par_init,x,y)
      9     cons = make_constraints(par_min,par_max)
     10 
---> 11     opt_pars = opt.fmin_cobyla(cost_func,par_init,cons,args=([y,x]))
     12     return opt_pars 

/home/luke/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/cobyla.pyc in fmin_cobyla(func, x0, cons, args, consargs, rhobeg, rhoend, iprint, maxfun, disp, catol)
    169 
    170     sol = _minimize_cobyla(func, x0, args, constraints=con,
--> 171                            **opts)
    172     if iprint > 0 and not sol['success']:
    173         print("COBYLA failed to find a solution: %s" % (sol.message,))

/home/luke/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/cobyla.pyc in _minimize_cobyla(fun, x0, args, constraints, rhobeg, tol, iprint, maxiter, disp, catol, **unknown_options)
    244     xopt, info = _cobyla.minimize(calcfc, m=m, x=np.copy(x0), rhobeg=rhobeg,
    245                                   rhoend=rhoend, iprint=iprint, maxfun=maxfun,
--> 246                                   dinfo=info)
    247 
    248     if info[3] > catol:

/home/luke/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/cobyla.pyc in calcfc(x, con)
    238         f = fun(x, *args)
    239         for k, c in enumerate(constraints):
--> 240             con[k] = c['fun'](x, *c['args'])
    241         return f
    242 

TypeError: <lambda>() takes exactly 1 argument (3 given)

这个错误对我来说并不完全清楚，但我的理解是3个参数被传递给我的约束函数。但是，我无法解决这三个论点的来源。

我已经查看过有关此问题的其他stackoverflow问题并从中获取了我可以使用的内容，但我仍然遇到此问题

Specifying constraints for fmin_cobyla in scipy

Python SciPy: optimization issue fmin_cobyla : one constraint is not respected

Python: how to create many constraints for fmin_cobyla optimization using lambda functions

Answer 1

如果consargs的参数fmin_cobyla为None，则约束函数也会传递*args，其中args是赋予{{1}的参数}}。要不向约束函数传递其他参数，请使用fmin_cobyla。

或者，在函数consargs=()中，更改此

make_constraints

到

        lower = lambda x: x[i] - a
        upper = lambda x: b - x[i]

在scipy.optimize.fmin_cobyla中指定约束以进行参数估计时出错

1 个答案: