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fixing mixed data in multiprocessing

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This commit is contained in:
Remi Ehounou
2021-09-11 17:22:51 -04:00
parent b05666e137
commit e99c9762ea
1 changed files with 6 additions and 5 deletions
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11
MDAF/MDAF.py
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@ -47,7 +47,7 @@ class counter:
self.count += 1 self.count += 1
return self.func(*args, **kwargs) return self.func(*args, **kwargs)
def simulate(algName, algPath, funcname, funcpath, args, initpoint): def simulate(algName, algPath, funcname, funcpath, heuris_args, initpoint):
# loading the heuristic object into the namespace and memory # loading the heuristic object into the namespace and memory
spec = importlib.util.spec_from_file_location(algName, algPath) spec = importlib.util.spec_from_file_location(algName, algPath)
heuristic = importlib.util.module_from_spec(spec) heuristic = importlib.util.module_from_spec(spec)
@ -68,7 +68,7 @@ def simulate(algName, algPath, funcname, funcpath, args, initpoint):
#This timer calculates directly the CPU time of the process (Nanoseconds) #This timer calculates directly the CPU time of the process (Nanoseconds)
tic = time.process_time_ns() tic = time.process_time_ns()
# running the test by calling the heuritic script with the test function as argument # running the test by calling the heuritic script with the test function as argument
quality = heuristic.main(testfunc, initpoint, args) quality = heuristic.main(testfunc, initpoint, heuris_args)
toc = time.process_time_ns() toc = time.process_time_ns()
# ^^ The timer ends right above this; the CPU time is then calculated below by simple difference ^^ # ^^ The timer ends right above this; the CPU time is then calculated below by simple difference ^^
@ -259,14 +259,15 @@ def doe(heuristicpath, testfunctionpaths, heuristic_args, measurementSampleSize
# Creating the connection objects for communication between the heuristic and this module # Creating the connection objects for communication between the heuristic and this module
connections[funcname] = multiprocessing.Pipe(duplex=False) connections[funcname] = multiprocessing.Pipe(duplex=False)
proc.append(multiprocessing.Process(target=measure, name=funcname, args=(heuristicpath, funcpath, heuristic_args, connections[funcname][1], measurementSampleSize))) proc.append(multiprocessing.Process(target=measure, name=funcname, args=(heuristicpath, funcpath, heuristic_args, connections[funcname][1], measurementSampleSize)))
proc[idx].start()
# defining the response variables # defining the response variables
responses = {} responses = {}
failedfunctions = {} failedfunctions = {}
# Starting the subprocesses for each testfunction # Starting the subprocesses for each testfunction
for idx,process in enumerate(proc): #for idx,process in enumerate(proc):
process.start() #process.start()
# Waiting for all the runs to be done # Waiting for all the runs to be done
for process in proc: process.join() for process in proc: process.join()
@ -375,7 +376,7 @@ if __name__== "__main__":
#visualize2D('@Easom.py', -10,10) #visualize2D('@Easom.py', -10,10)
#feats = array([representfunc(testfun, True)['ela_meta'] for testfun in testfuns]) #feats = array([representfunc(testfun, True)['ela_meta'] for testfun in testfuns])
#plotfuncs(['@Bukin2.py','@Bukin6.py'], 'ela_meta') #plotfuncs(['@Bukin2.py','@Bukin6.py'], 'ela_meta')
perf = doe('@SimmulatedAnnealing.py', testfuns[4:5],{"t": 1000, "p": 0.95, "objs": 0, "lower": [-10], "upper": [10]},measurementSampleSize=2) perf = doe('@SimmulatedAnnealing.py', testfuns[1:3],{"t": 1000, "p": 0.95, "objs": 0, "lower": [-10], "upper": [10]},measurementSampleSize=2)
#perfs = array([[perf[func][0]['cpuTime'][0], perf[func][0]['numCalls'][0], perf[func][0]['quality'][0], perf[func][0]['convRate'][0]] for func in perf.keys()]) #perfs = array([[perf[func][0]['cpuTime'][0], perf[func][0]['numCalls'][0], perf[func][0]['quality'][0], perf[func][0]['convRate'][0]] for func in perf.keys()])
#features = array(feats) #features = array(feats)