如何显示 scypy.Optimfunction 的进度？

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)

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

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

Which minimization function are you using exactly?

Most of the functions have progress report built, including multiple levels of reports showing exactly the data you want, by using the disp flag (for example see scipy.optimize.fmin_l_bfgs_b).

Following @joel's example, there is a neat and efficient way to do the similar thing. Following example show how can we get rid of global variables, call_back functions and re-evaluating target function multiple times.

import numpy as np
from scipy.optimize import fmin_bfgs


def rosen(X, info): #Rosenbrock function
res = (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




# display information
if info['Nfeval']%100 == 0:
print '{0:4d}   {1: 3.6f}   {2: 3.6f}   {3: 3.6f}   {4: 3.6f}'.format(info['Nfeval'], X[0], X[1], X[2], res)
info['Nfeval'] += 1
return res


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,
args=({'Nfeval':0},),
maxiter=1000,
full_output=True,
retall=False,
)

This will generate output like

Iter    X1          X2          X3         f(X)
0    1.100000    1.100000    1.100000    2.440000
100    1.000000    0.999999    0.999998    0.000000
200    1.000000    0.999999    0.999998    0.000000
300    1.000000    0.999999    0.999998    0.000000
400    1.000000    0.999999    0.999998    0.000000
500    1.000000    0.999999    0.999998    0.000000
Warning: Desired error not necessarily achieved due to precision loss.
Current function value: 0.000000
Iterations: 12
Function evaluations: 502
Gradient evaluations: 98

However, no free launch, here I used function evaluation times instead of algorithmic iteration times as a counter. Some algorithms may evaluate target function multiple times in a single iteration.

Try using:

options={'disp': True}

to force scipy.optimize.minimize to print intermediate results.

Below is a solution that works for me :

def f_(x):   # The rosenbrock function
return (1 - x[0])**2 + 100 * (x[1] - x[0]**2)**2


def conjugate_gradient(x0, f):
all_x_i = [x0[0]]
all_y_i = [x0[1]]
all_f_i = [f(x0)]
def store(X):
x, y = X
all_x_i.append(x)
all_y_i.append(y)
all_f_i.append(f(X))
optimize.minimize(f, x0, method="CG", callback=store, options={"gtol": 1e-12})
return all_x_i, all_y_i, all_f_i

and by example :

conjugate_gradient([2, -1], f_)

Source

Many of the optimizers in scipy indeed lack verbose output (the 'trust-constr' method of scipy.optimize.minimize being an exception). I faced a similar issue and solved it by creating a wrapper around the objective function and using the callback function. No additional function evaluations are performed here, so this should be an efficient solution.

import numpy as np


class Simulator:
def __init__(self, function):
self.f = function # actual objective function
self.num_calls = 0 # how many times f has been called
self.callback_count = 0 # number of times callback has been called, also measures iteration count
self.list_calls_inp = [] # input of all calls
self.list_calls_res = [] # result of all calls
self.decreasing_list_calls_inp = [] # input of calls that resulted in decrease
self.decreasing_list_calls_res = [] # result of calls that resulted in decrease
self.list_callback_inp = [] # only appends inputs on callback, as such they correspond to the iterations
self.list_callback_res = [] # only appends results on callback, as such they correspond to the iterations


def simulate(self, x, *args):
"""Executes the actual simulation and returns the result, while
updating the lists too. Pass to optimizer without arguments or
parentheses."""
result = self.f(x, *args) # the actual evaluation of the function
if not self.num_calls: # first call is stored in all lists
self.decreasing_list_calls_inp.append(x)
self.decreasing_list_calls_res.append(result)
self.list_callback_inp.append(x)
self.list_callback_res.append(result)
elif result < self.decreasing_list_calls_res[-1]:
self.decreasing_list_calls_inp.append(x)
self.decreasing_list_calls_res.append(result)
self.list_calls_inp.append(x)
self.list_calls_res.append(result)
self.num_calls += 1
return result


def callback(self, xk, *_):
"""Callback function that can be used by optimizers of scipy.optimize.
The third argument "*_" makes sure that it still works when the
optimizer calls the callback function with more than one argument. Pass
to optimizer without arguments or parentheses."""
s1 = ""
xk = np.atleast_1d(xk)
# search backwards in input list for input corresponding to xk
for i, x in reversed(list(enumerate(self.list_calls_inp))):
x = np.atleast_1d(x)
if np.allclose(x, xk):
break
    

for comp in xk:
s1 += f"{comp:10.5e}\t"
s1 += f"{self.list_calls_res[i]:10.5e}"


self.list_callback_inp.append(xk)
self.list_callback_res.append(self.list_calls_res[i])


if not self.callback_count:
s0 = ""
for j, _ in enumerate(xk):
tmp = f"Comp-{j+1}"
s0 += f"{tmp:10s}\t"
s0 += "Objective"
print(s0)
print(s1)
self.callback_count += 1

A simple test can be defined

from scipy.optimize import minimize, rosen
ros_sim = Simulator(rosen)
minimize(ros_sim.simulate, [0, 0], method='BFGS', callback=ros_sim.callback, options={"disp": True})


print(f"Number of calls to Simulator instance {ros_sim.num_calls}")

resulting in:

Comp-1          Comp-2          Objective
1.76348e-01     -1.31390e-07    7.75116e-01
2.85778e-01     4.49433e-02     6.44992e-01
3.14130e-01     9.14198e-02     4.75685e-01
4.26061e-01     1.66413e-01     3.52251e-01
5.47657e-01     2.69948e-01     2.94496e-01
5.59299e-01     3.00400e-01     2.09631e-01
6.49988e-01     4.12880e-01     1.31733e-01
7.29661e-01     5.21348e-01     8.53096e-02
7.97441e-01     6.39950e-01     4.26607e-02
8.43948e-01     7.08872e-01     2.54921e-02
8.73649e-01     7.56823e-01     2.01121e-02
9.05079e-01     8.12892e-01     1.29502e-02
9.38085e-01     8.78276e-01     4.13206e-03
9.73116e-01     9.44072e-01     1.55308e-03
9.86552e-01     9.73498e-01     1.85366e-04
9.99529e-01     9.98598e-01     2.14298e-05
9.99114e-01     9.98178e-01     1.04837e-06
9.99913e-01     9.99825e-01     7.61051e-09
9.99995e-01     9.99989e-01     2.83979e-11
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 19
Function evaluations: 96
Gradient evaluations: 24
Number of calls to Simulator instance 96

Of course this is just a template, it can be adjusted to your needs. It does not provide all information about the status of the optimizer (like e.g. in the Optimization Toolbox of MATLAB), but at least you have some idea of the progress of the optimization.

A similar approach can be found here, without using the callback function. In my approach the callback function is used to print output exactly when the optimizer has finished an iteration, and not every single function call.

It is also possible to include a simple print() statement in the function to be minimized. If you import the function you can create a wapper.

import numpy as np
from scipy.optimize import minimize




def rosen(X): #Rosenbrock function
print(X)
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


x0 = np.array([1.1, 1.1, 1.1], dtype=np.double)
minimize(rosen,
x0)

There you go! (Beware: Most of the time, global variables are bad practice.)

from scipy.optimize import minimize
import numpy as np


f = lambda x, b=.1 : x[0]**2 + b * x[1]**2


x0 = np.array( [2.,2.] )
P = [ x0 ]


def save(x):
global P
P.append(x)
    

minimize(f, x0=x0, callback=save)

      fun: 4.608946876190852e-13
hess_inv: array([[4.99995194e-01, 3.78976566e-04],
[3.78976566e-04, 4.97011817e+00]])
jac: array([ 5.42429092e-08, -4.27698767e-07])
message: 'Optimization terminated successfully.'
nfev: 24
nit: 7
njev: 8
status: 0
success: True
x: array([ 1.96708740e-08, -2.14594442e-06])

print(P)

[array([2., 2.]),
array([0.99501244, 1.89950125]),
array([-0.0143533,  1.4353279]),
array([-0.0511755 ,  1.11283405]),
array([-0.03556007,  0.39608524]),
array([-0.00393046, -0.00085631]),
array([-0.00053407, -0.00042556]),
array([ 1.96708740e-08, -2.14594442e-06])]