The modestga from krzysztofarendt

modestga's Issues

Minimization does not return on Linux

end_event is not set True on Linux (WSL2).

Adaptive mutation and cross-over rate

Mutation based on normal distribution (to help with local optimization)
Mutation rate adaptive
Cross-over rate adaptive or random

Serialization of exotic functions fails on Windows

The following unit test fails on Windows:

======================================================================
ERROR: test_exotic_pickling (__main__.TestModestga)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "modestga\test\test.py", line 233, in test_exotic_pickling
    workers=2)
  File "d:\code\modestga\modestga\ga.py", line 167, in minimize
    p.start()
  File "D:\Anaconda3\envs\modestga\lib\multiprocessing\process.py", line 112, in start
    self._popen = self._Popen(self)
  File "D:\Anaconda3\envs\modestga\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "D:\Anaconda3\envs\modestga\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "D:\Anaconda3\envs\modestga\lib\multiprocessing\popen_spawn_win32.py", line 89, in __init__
    reduction.dump(process_obj, to_child)
  File "D:\Anaconda3\envs\modestga\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'TestModestga.test_exotic_pickling.<locals>.FunWrapper'

----------------------------------------------------------------------

On Linux it works fine.

Add number of function evaluation nfev

Add nfev to OptRes.

Examples fail on Windows

When running examples using multiprocessing on Windows, the following errors pops out:

RuntimeError:                                                                                                                                                                                                                                                           An attempt has been made to start a new process before the                                                                                                                                                                                                      current process has finished its bootstrapping phase.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           This probably means that you are not using fork to start your                                                                                                                                                                                                   child processes and you have forgotten to use the proper idiom                                                                                                                                                                                                  in the main module:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 if __name__ == '__main__':                                                                                                                                                                                                                                          freeze_support()                                                                                                                                                                                                                                                ...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     The "freeze_support()" line can be omitted if the program                                                                                                                                                                                                       is not going to be frozen to produce an executable.

It is because Windows requires the code using multiprocessing to be enclosed with the condition:
if __name__ == '__main__'.

The examples work fine on Linux.

There should warning about this also in README.

Rastrigin funcion example doesn't work

krza@DESKTOP-3738N1E:~/code/modestga$ python modestga/examples/min_rastrigin_fun.py
Traceback (most recent call last):
  File "modestga/examples/min_rastrigin_fun.py", line 37, in <module>
    res = minimize(fun, bounds, args=args, callback=callback, options=options)
  File "/home/krza/code/modestga/modestga/ga.py", line 171, in minimize
    pop = population.Population(opts['pop_size'], bounds, fun, args=args, evaluate=True)
  File "/home/krza/code/modestga/modestga/population.py", line 28, in __init__
    val=val
  File "/home/krza/code/modestga/modestga/individual.py", line 51, in __init__
    self.val = self.evaluate()
  File "/home/krza/code/modestga/modestga/individual.py", line 68, in evaluate
    self.val = self.fun(self.get_estimates(), *self.args)
TypeError: rastrigin() takes 1 positional argument but 3 were given

Rewrite benchmark framework

Multiple benchmark cases are likely to be added in the future. They all should be put in some kind of a framework with YAML configuration files.

Argument passing fails with workers > 1

Failing test:

======================================================================
ERROR: test_args_passing_2workers (__main__.TestModestga)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "modestga/test/test.py", line 209, in test_args_passing_2workers
    workers=2)
  File "/home/krza/code/modestga/modestga/ga.py", line 258, in minimize
    data_from.append(pipes[i].recv())
  File "/usr/lib/python3.6/multiprocessing/connection.py", line 250, in recv
    buf = self._recv_bytes()
  File "/usr/lib/python3.6/multiprocessing/connection.py", line 407, in _recv_bytes
    buf = self._recv(4)
  File "/usr/lib/python3.6/multiprocessing/connection.py", line 383, in _recv
    raise EOFError
EOFError

----------------------------------------------------------------------

Function evaluation counter in GA seems to be broken

Results from 70 cases using Rastrigin function with varying number of parameters:

Used settings (those other than default included only):

    options = {
        'generations': 1000,  # Max. number of generations
        'pop_size': 100,      # Population size
        'tol': 1e-3           # Solution tolerance
    }

Add cloudpickle to setup.py requirements

Exception handling in function evaluation

We cannot guarantee that the cost function will work for the entire range of parameters. In example in ModestPy FMU solvers sometimes fail when unphysical parameters are used (see: sdu-cfei/modest-py#68).

It should be fairly easy to implement some generic exception handling. I think it is sufficient to enclose the following line with a try... except... block:

modestga/modestga/individual.py

Line 68 in 46a6bde

self.val = self.fun(self.get_estimates(), *self.args)

When exception is caught the function value could be +infinity or some very large number.

Add support to multiprocessing

Use Remote Manager and Proxy Objects to share the function to be minimized accross processes.
This will enable to minimize unpickable functions in parallel.

References:

Remove jupyter notebooks

Inter-method comparisons should be implemented in pure Python scripts. Remove notebooks from modestga/benchmark.

Dill support

Consider https://github.com/uqfoundation/dill in favor of cloudpickle.

Release v0.4

Algorithm stops when fx <= 0

The algorithm doesn't work with negative cost function values. It stops when reaching a value near zero.

Exception handling or time out needed in the parallel mode

When an exception happens in one of the threads, the main process waits indefinitely for its result. A timeout or exception handling should be implemented.

An example from one of the users of ModestPy:

Traceback (most recent call last):

  File "C:\Users\lucjsar\Documents\ModestPy\lucile_GBM_max_flows.py", line 151, in <module>
    estimates = session.estimate()

  File "C:\Users\lucjsar\Documents\ModestPy\modestpy\estimation.py", line 289, in estimate
    m_estimates = m_inst.estimate()

  File "C:\Users\lucjsar\Documents\ModestPy\modestpy\estim\ga_parallel\ga_parallel.py", line 315, in estimate
    workers=self.workers)

  File "C:\Users\lucjsar\AppData\Local\Continuum\anaconda3\lib\site-packages\modestga\ga.py", line 257, in minimize
    if pipes[i].poll(0.001):

  File "C:\Users\lucjsar\AppData\Local\Continuum\anaconda3\lib\multiprocessing\connection.py", line 257, in poll
    return self._poll(timeout)

 File "C:\Users\lucjsar\AppData\Local\Continuum\anaconda3\lib\multiprocessing\connection.py", line 330, in _poll
    return bool(wait([self], timeout))

  File "C:\Users\lucjsar\AppData\Local\Continuum\anaconda3\lib\multiprocessing\connection.py", line 868, in wait
    ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)

  File "C:\Users\lucjsar\AppData\Local\Continuum\anaconda3\lib\multiprocessing\connection.py", line 800, in _exhaustive_wait
    res = _winapi.WaitForMultipleObjects(L, False, timeout)

Add minimal working example (https://github.com/krzysztofarendt/modestga/blob/issue-9/soft-constraints/modestga/constr_min.py)
Add unit tests
Add example
Add more benchmarks
Remove examples from if __name__ == "__main__":
Tune algorithm for the Rosenbrock example (it struggles to reach the global optimum at (1, 1))
Update README
Merge with the branch v0.6

Benchmark GA against other methods

Compare the performance of the implemented GA with other available methods, especially Monte Carlo (own implementation) and Differential Evolution (from SciPy).

args not passed to the cost function?

I'm not sure if args are passed to the cost function.

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