如何显示scipy.optimize函数的进度? [英] How to display progress of scipy.optimize function?

问题描述

我使用scipy.optimize来最小化12个参数的函数.

I use scipy.optimize to minimize a function of 12 arguments.

我前一段时间开始优化，但仍在等待结果.

I started the optimization a while ago and still waiting for results.

是否有一种方法可以强制scipy.optimize显示其进度(例如已经完成了多少，当前的最佳点是什么)?

Is there a way to force scipy.optimize to display its progress (like how much is already done, what are the current best point)?

推荐答案

正如mg007所建议的，某些scipy.optimize例程允许使用回调函数(不幸的是，smastsq目前不允许这样做).下面是使用"fmin_bfgs"例程的示例，在该例程中，我使用回调函数在每次迭代时显示参数的当前值和目标函数的值.

As mg007 suggested, some of the scipy.optimize routines allow for a callback function (unfortunately leastsq does not permit this at the moment). Below is an example using the "fmin_bfgs" routine where I use a callback function to display the current value of the arguments and the value of the objective function at each iteration.

import numpy as np
from scipy.optimize import fmin_bfgs

Nfeval = 1

def rosen(X): #Rosenbrock function
    return (1.0 - X[0])**2 + 100.0 * (X[1] - X[0]**2)**2 + \
           (1.0 - X[1])**2 + 100.0 * (X[2] - X[1]**2)**2

def callbackF(Xi):
    global Nfeval
    print '{0:4d}   {1: 3.6f}   {2: 3.6f}   {3: 3.6f}   {4: 3.6f}'.format(Nfeval, Xi[0], Xi[1], Xi[2], rosen(Xi))
    Nfeval += 1

print  '{0:4s}   {1:9s}   {2:9s}   {3:9s}   {4:9s}'.format('Iter', ' X1', ' X2', ' X3', 'f(X)')   
x0 = np.array([1.1, 1.1, 1.1], dtype=np.double)
[xopt, fopt, gopt, Bopt, func_calls, grad_calls, warnflg] = \
    fmin_bfgs(rosen, 
              x0, 
              callback=callbackF, 
              maxiter=2000, 
              full_output=True, 
              retall=False)

输出看起来像这样:

The output looks like this:

Iter    X1          X2          X3         f(X)      
   1    1.031582    1.062553    1.130971    0.005550
   2    1.031100    1.063194    1.130732    0.004973
   3    1.027805    1.055917    1.114717    0.003927
   4    1.020343    1.040319    1.081299    0.002193
   5    1.005098    1.009236    1.016252    0.000739
   6    1.004867    1.009274    1.017836    0.000197
   7    1.001201    1.002372    1.004708    0.000007
   8    1.000124    1.000249    1.000483    0.000000
   9    0.999999    0.999999    0.999998    0.000000
  10    0.999997    0.999995    0.999989    0.000000
  11    0.999997    0.999995    0.999989    0.000000
Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 11
         Function evaluations: 85
         Gradient evaluations: 17

至少您可以通过这种方式观察优化器跟踪最小值的情况

At least this way you can watch as the optimizer tracks the minimum

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