How to store set of parameters when optimization crashes? about blackbox

[paulknysh]

Hi,
From what I understood, what fails is your simulation (= objective function), not the optimization procedure itself. If that's the case and you want to identify parameters for which it fails, you can program your objective function so that it returns 1 when simulation works and 0 when it fails. Or, even better, make your objective function return some measure of how singular your matrix is (maybe determinant?).

Hello.
I think if I knew how the optimization code is working in parallel then I may be able to find when the simulation is failing. Since the optimization code working in parallel, I am not sure how minimization function (objective function in bb.search( )) is being called unlike in series where I could just define a global variable (say par = [ ]) and append the objective function parameters to it each time optimization calls the objective function with new set of parameters as following:

global par
par = []
def fn_ob(param):
    par.append(param)
    out_estimated = foo(param)
    obj = np.sum((out_estimated - out_match)**2)
    return obj

bb.search(f=fn_obj, ...)

The above code will store all the parameters if the optimization calls the objective function in series and I would get all parameters in par that have been tried by the optimization even if overall optimization crashes at some point because of simulation error in foo(param_i) for some param_i. However, I am not sure how this will work in case of parallel optimization with blackbox. Can you help how I can do this with blackbox? Thanks.

[paulknysh]

So, the important question is what happens when your simulation fails. If you can control that failure inside of your objective function (say, simulation returns some kind of error code that you can capture), then you can make your function look like this:

def fun(par):
    ...
    if errorcode == 'Simulation fails': # or whatever it is
        return 0.
    else:
        return abs(det) # determinant or whatever tells how poor your matrices are 

I hope you understand that failure of your simulation is a side effect that has nothing to do with optimization itself. So you need to control that side effect somehow. The objective function needs to be returning some value, no matter if simulation works or fails.

If failure of the simulation is causing some bad issues (memory/system crush etc) that you cannot control, then you need to go the guts of the blackbox.py and save the intermediate results into a file or display them. The array points is what you need to look at, even though it might not be easy to analyze because there are few scaling tricks applied to that array at several spots.

I strongly recommend you going the first way if possible - making your objective function immune to the failure of the simulations.

[paulknysh]

As for what you propose (saving a current set of parameters into a global list), that's a good idea, but I'm not sure how this will interfere with parallelism that is used in blackbox.py. You can try, but I cannot guarantee that it will work correctly. You can at least try on some simple (known) function first.

The simulation is not easy to track because I am running nested simulations (two different types) with a large number of variables, and the optimization parameters are indirectly (not directly) affecting the simulation that's producing errors...it's the first simulation that produces some output that goes into the second simulation input.

Simple question: How would you store all the parameters that the blackbox optimization is using in parallel?

[paulknysh]

You can try your idea (with the global list). If the parallelism is not very crucial, you can set batch=1 when you call the optimization procedure, which means only one function evaluation will be performed at a time (= no parallelism).

Also, you can make your objective function simply print the current set of parameters on the screen. I think this should work well even when you have parallelism.

Finally, as I mentioned, you can go inside of blackbox.py and output the current values of array points. But again, you would need to spend some time understanding how the code works (that array is modified at few spots).

I have already tried what you are suggesting, but it doesn't help. For instance, I print the parameters on screen, but I wouldn't know which set of parameters is causing the error because each core is running the code in parallel and I have no idea which (and how many) set of parameters are clubbed together for each core. That is important to narrow down to the set that's causing the problem.

Latest update: I did save the parameters that the optimization uses for all scenarios by some ad-hoc, and inefficient method, and I didn't seem to have any error when I ran the simulation on those set of parameters individually outside the optimization process. Therefore, it's possible there is some problem with the optimization code itself...

Dear Paul,

How can I include constraints in bb.search( )? I looked at the code and it seems like you're calling scipy.optimize.minimize( ), so I am assuming the constraints can be passed to bb.search( ) just as they would be passed to scipy.optimize.minimize( ) function?
Thanks.

[paulknysh]

The code doesn't support constraints.