multiprocessing with pickled sampling / test

SourceCode/AlgorithmAnalyser.py

@@ -1,13 +1,14 @@
 # directly running the DOE because existing surrogates can be explored with another workflow
 from os import path
 import importlib.util
-import multiprocessing
+import  multiprocessing
+import pathos.multiprocessing as mp
 import time
 import re
 from numpy import random as r
 from numpy import *
 import statistics
-
+from functools import partial
 import shutil
 
 # Surrogate modelling
@@ -31,7 +32,41 @@ class counter:
         self.count += 1
         return self.func(*args, **kwargs)
 
+def simulate(algName, algPath, funcname, funcpath, objs, args, initpoint):
+    # loading the heuristic object into the namespace and memory
+    spec = importlib.util.spec_from_file_location(algName, algPath)
+    heuristic = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(heuristic)
 
+    # loading the test function object into the namespace and memory
+    testspec = importlib.util.spec_from_file_location(funcname, funcpath)
+    func = importlib.util.module_from_spec(testspec)
+    testspec.loader.exec_module(func)
+
+    # defining a countable test function
+    @counter
+    def testfunc(args):
+        return func.main(args)
+
+    # using a try statement to handle potential exceptions raised by child processes like the algorithm or test functions or the pooling algorithm
+    try:
+        #This timer calculates directly the CPU time of the process (Nanoseconds)
+        tic = time.process_time_ns()
+        # running the test by calling the heuritic script with the test function as argument
+        quality = heuristic.main(testfunc, objs, initpoint, args)
+        toc = time.process_time_ns()
+        # ^^ The timer ends right above this; the CPU time is then calculated below by simple difference ^^
+
+        # CPU time in seconds
+        cpuTime = (toc - tic)*(10**-9)
+        numCalls = testfunc.count
+        converged = 1
+    except:
+        quality = NaN
+        cpuTime = NaN
+        numCalls = testfunc.count
+        converged = 0
+    return cpuTime, quality, numCalls, converged
 
 def measure(heuristicpath, heuristic_name, funcpath, funcname, objs, args, scale, connection):
     '''
@@ -41,56 +76,26 @@ def measure(heuristicpath, heuristic_name, funcpath, funcname, objs, args, scale
     # Seeding the random module for generating the initial point of the optimizer: Utilising random starting point for experimental validity
     r.seed(int(time.time()))
 
-    # loading the heuristic object into the namespace and memory
-    spec = importlib.util.spec_from_file_location(heuristic_name, heuristicpath)
-    heuristic = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(heuristic)
 
-    testspec = importlib.util.spec_from_file_location(funcname, funcpath)
-    func = importlib.util.module_from_spec(testspec)
-    testspec.loader.exec_module(func)
+    # Defining random initial points to start testing the algorithms
+    initpoints = [[r.random() * scale, r.random() * scale] for run in range(3)] #update the inner as [r.random() * scale for i in range(testfuncDimmensions)]
+    # building the iterable arguments
+    partfunc = partial(simulate, heuristic_name, heuristicpath, funcname, funcpath, objs, args)
 
-    responses = array([0,0])
-    convergence = empty(0)
-    best = empty(0)
+    with multiprocessing.Pool(processes = 3) as pool:
+        # running the simulations
+        newRun = pool.map(partfunc,initpoints)
         
-    with pool(processes = 10) as pool:
+    cpuTime = [resl[0] for resl in newRun]
+    quality = [resl[1] for resl in newRun]
+    numCalls = [resl[2] for resl in newRun]
+    converged = [resl[3] for resl in newRun]
     
-        for run in range(30):
-            # defining a countable test function
-            @counter
-            def testfunc(args):
-                return func.main(args)
-            # Defining a random initial point to start testing the algorithms
-            initpoint = [r.random() * scale, r.random() * scale]
-
-            try:
-                #This timer calculates directly the CPU time of the process (Nanoseconds)
-                tic = time.process_time_ns()
-                # running the test by calling the heuritic script with the test function as argument
-                best = append(best, heuristic.main(testfunc, objs, initpoint, args))
-                toc = time.process_time_ns()
-                # ^^ The timer ends right above this; the CPU time is then calculated below by simple difference ^^
-                # CPU time in seconds
-                cpuTime = (toc - tic)*(10**-9)
-                numCalls = testfunc.count
-                converged = 1
-                
-            except:
-                best = NaN
-                cpuTime = NaN
-                numCalls = testfunc.count
-                converged = 0
-            
-            # Building the response
-            responses = vstack([responses, array([cpuTime,numCalls])])
-            convergence = append(convergence,[converged])
-        
-    responses = delete(responses,[0],axis=0)
     results = dict()
-    results['stdevs'] = array([statistics.stdev(responses[:,[0]].flatten()), statistics.stdev(responses[:,[1]].flatten())])
-    results['means'] = array([statistics.mean(responses[:,[0]].flatten()), statistics.mean(responses[:,[1]].flatten())])
-    results['convrate'] = statistics.mean(convergence)
+    results['cpuTime'] = array([statistics.mean(cpuTime), statistics.stdev(cpuTime)])
+    results['quality'] = array([statistics.mean(quality), statistics.stdev(quality)])
+    results['numCalls'] = array([statistics.mean(numCalls), statistics.stdev(numCalls)])
+    results['convRate'] = array([statistics.mean(converged), statistics.stdev(converged)])
 
     connection.send(results)
 
@@ -220,7 +225,6 @@ def representfunc(funcpath):
             ax3.imshow(beta)
             
             plt.show()
-            print("should be plotted")
             
             # Writing the calculated representation into the test function file
             # results['Represented'] = True
@@ -269,7 +273,7 @@ def doe(heuristicpath, heuristic_name, testfunctionpaths, funcnames, objs, args,
         connections[run][1].close()
 
     # display output
-    print("\n\n||||| Responses: [cpuTime,numCalls] |||||")
+    print("\n\n||||| Responses: [mean,stdDev] |||||")
     for process in proc: print(process.name + "____\n" + str(responses[process.name]) + "\n_________________")
 
 if __name__ == '__main__':

SourceCode/SampleAlgorithms/SimmulatedAnnealing.py

@@ -77,7 +77,7 @@ def main(func, obj, S, args):
     #print('the Best Quality obtained was:{}'.format(Quality(Best,y)))
     print("Final Quality is: {}".format(Quality(Best,y,func)))
     print("final Temperature is: {}".format(t))
-    return Best
+    return Quality(Best,y,func)