fix: Fixed single_run and added multiple_tr_chaining

 - Created new method for Iterated Chaining method (also writes to a file)
- Fixed single_run function to now return the NFE value.
- Silenced all mid-run print_schedule and plot_scores, plot_fig all to False ( now all runs happen silently without printing or popping with a plot in btwn runs' did not match any file(s) known to git

flightscheduling.py

@@ -15,8 +15,8 @@
 from fitness import *
 from utils.ga_utils import multi_mutation, mutation
 from utils.utils import people, plot_scores, print_schedule, read_file, time
-
-matplotlib.use('TKAgg')
+    
+matplotlib.use('TKAgg') # Remove if not using WSL
 
 _FLIGHTS_FILE_='flights.txt'
 
@@ -66,6 +66,21 @@ def multiple_runs(algorithm,domain,fitness_function, init=[], use_multiproc=Fals
         f.close()
         print("Total time ", round(sum(times), 3))
 
+def multiple_runs_itr_chaining(algorithm_1, algorithm_2,domain,fitness_function,seed=random.randint(10,100), rounds=10 , n_obs=2, tol=90, save_fig=False,n=20):
+    res=list([])
+    seeds=[10,24,32,100,20,67,13,19,65,51,35,61,154,85,144,162,48,79,69,186]
+    if n>0 and n<20:
+            seeds=seeds[:n]
+    for i in seeds:
+        start=time.time()
+        final_soln,cost,scores,nfe = sol_chaining(algorithm_1,algorithm_2 ,domain,fitness_function,save_fig=True,seed=i,rounds=rounds,n_obs=n_obs,tol=tol)
+        res.append([cost,time.time()-start,nfe])
+
+    with open('./results/multi_proc/fitness_function/iterated_solution_chaining.csv','w+') as f:
+        f.write("Cost"+","+"Run_Time"+","+"NFE"+"\n")
+        for item in res:
+            f.write(str(item[0]) + "," + str(item[1]) +"," + str(item[2])+ "\n")
+
 
 def single_run(algorithm,domain,fitness_function,init=[],seed=random.randint(10,100),seed_init=True,save_fig=False, print_sch=True): 
     global scores
@@ -81,66 +96,69 @@ def single_run(algorithm,domain,fitness_function,init=[],seed=random.randint(10,
 
     if print_sch and fitness_function.__name__ == 'fitness_function':
         print_schedule(soln, 'FCO')
-    return soln, cost
+    return soln, cost,nfe
 
 def sol_chaining(algorithm_1, algorithm_2,domain,fitness_function,seed=random.randint(10,100), rounds=10 , n_obs=2, tol=90, save_fig=False):
    # Note scores here is the best cost of each particular single_run
     scores = []
+    NFE=0
     for i in range(rounds):
         if i == 0:
-            soln, cost = single_run(algorithm_1,domain,fitness_function, print_sch=False,seed=seed)
+            soln, cost,nfe = single_run(algorithm_1,domain,fitness_function,save_fig=True, print_sch=False,seed=seed)
             soln=mutation(domain,random.randint(0,1),soln) # Either 1 step or no step InitMutation
             #soln=multi_mutation(domain,1,soln)
             scores.append(cost)
+            NFE+=nfe
             print("Cost at {}=={}".format(i, cost))
         elif i == rounds-1:
-            final_soln, cost = single_run(
-                algorithm_2,domain=domain,fitness_function=fitness_function, init=soln, save_fig=False, print_sch=True,seed=seed)
+            final_soln, cost,nfe = single_run(
+                algorithm_2,domain=domain,fitness_function=fitness_function, init=soln, save_fig=True, print_sch=False)
             scores.append(cost)
+            NFE+=nfe
             print("Cost at {}=={}".format(i, cost))
             plot_scores(scores, sol_chaining.__name__, save_fig)
-            return final_soln, scores[-1], scores
+            return final_soln, scores[-1], scores,NFE
         else:
-            soln, cost = single_run(
-                algorithm_1,domain=domain,fitness_function=fitness_function, init=init, save_fig=False, print_sch=False,seed=seed)
+            soln, cost,nfe = single_run(
+                algorithm_1,domain=domain,fitness_function=fitness_function, init=init, save_fig=True, print_sch=False)
             print("Cost at {}=={}".format(i, cost))
             soln=mutation(domain,random.randint(0,1),soln)
             #soln=multi_mutation(domain,1,soln)
             scores.append(cost)
+            NFE+=nfe
 
-        final_soln, cost = single_run(algorithm_2,domain=domain,fitness_function=fitness_function, init=soln, print_sch=False,seed=seed)
+        final_soln, cost,nfe = single_run(algorithm_2,domain=domain,fitness_function=fitness_function, init=soln,save_fig=True, print_sch=False)
         scores.append(cost)
+        NFE+=nfe
         if cost - random.randint(tol, 100) > int(sum(scores[-n_obs:])/n_obs):
             print("----Ending early at iteration{}----".format(i))
             print("Cost{}".format(cost))
             if fitness_function.__name__ == 'fitness_function':
                 print_schedule(final_soln, 'FCO')
-            plot_scores(scores, sol_chaining.__name__, save_fig)
-            return final_soln, scores[-1], scores
+            #plot_scores(scores, sol_chaining.__name__, save_fig)
+            return final_soln, scores[-1], scores,NFE
         print("Cost at {}=={}".format(i, cost))
         init = mutation(domain,1,final_soln)  # IntMutation
 
 
 
 def main():
-    """CHANGES In order:
-    10. Exception handling
-
-    """
-    #print_schedule(soln,'FCO')
-    #multiple_runs(genetic_algorithm_with_reversals, n=20, use_multiproc=True,domain=domain['domain'],fitness_function=fitness_function)
-    #final_soln,cost,scores = sol_chaining(random_search,hill_climb ,domain=domain['domain'],fitness_function=fitness_function,save_fig=True,seed=10)
-    soln, cost = single_run(genetic_algorithm_with_reversals,domain['domain'],fitness_function,seed_init=False, save_fig=False, print_sch=True)
-    #print(soln,cost)
+    soln, cost,nfe = single_run(genetic_algorithm_with_reversals,domain['griewank']*13,griewank,seed_init=False, save_fig=False, print_sch=True)
+    print(soln,cost,nfe)
+    print_schedule(soln,'FCO')
     #multiple_runs(simulated_annealing,domain[ 'matyas'],matyas,n=20,use_multiproc=True)
     #multiple_runs(random_search,domain['matyas'],matyas,n=20,use_multiproc=True)
-    #multiple_runs(hill_climb,domain['matyas'],matyas,n=20,use_multiproc=True)
-    #multiple_runs(genetic_algorithm,domain['matyas'],matyas,n=20,use_multiproc=True)
-    #multiple_runs(genetic_algorithm_reversed,domain['matyas'],matyas,n=20,use_multiproc=True)
-    #multiple_runs(genetic_algorithm_with_reversals,domain['matyas'],matyas,n=20,use_multiproc=True)
+    #multiple_runs(hill_climb,domain['griewank']*13,griewank,n=20,use_multiproc=True)
+    #multiple_runs(genetic_algorithm,domain['three_hump_camel'],three_hump_camel,n=20,use_multiproc=True)
+    #multiple_runs(genetic_algorithm_reversed,domain['three_hump_camel'],three_hump_camel,n=20,use_multiproc=True)
+    #multiple_runs(genetic_algorithm_with_reversals,domain['three_hump_camel'],three_hump_camel,n=20,use_multiproc=True)
     #soln,cost=single_run(simulated_annealing,save_fig=False,print_sch=False,domain=domain['griewank']*13,fitness_function=griewank,seed=10)
 
 if __name__ == "__main__":
+    #CHANGES In order:
+    #    1. Exception handling
+    #    2. SHorten parameter names?
+    #    3. Change to multiproc functions/restructuring?
     main()