87 lines
No EOL
4.3 KiB
Python
Executable file
87 lines
No EOL
4.3 KiB
Python
Executable file
#!/usr/bin/env python3
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import sys
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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#get the distribution of operators variants recommended by 15 runs of irace for all problems and each problem
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#also get an csv file
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#pc and pm 10 possibilities : [0-0.1[ [0.1-0.2[ [0.2-0.3[ [0.3-0.4[ [0-0.5[ [0.5-0.6[ ...[0.9-1[
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#pop-size and offspring-size 10 possibilities : 0-5 5-10, 10-15 15-20 20-25 25-30 30-35- 35-40 40-45 45-50
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path=sys.argv[1] # directory of a result of one experiment
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#eg : ./fastga_results_all/fastga_results_planO/planO_maxExp\=100000_maxEv\=5n_2021-08-13T19\:04+02\:00_results_elites_all/
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if("fastga_results_plan" in path):
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#column : [operator : nbpossibilities]
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distdir=sys.argv[2]
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try:
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os.makedirs(distdir)
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except FileExistsError:
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pass
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nbparam=(len(os.listdir(os.path.join(path,"raw/data"))[0].split("_"))-1)
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if( nbparam==7):
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res=[{"crossover-rate":["pc" , np.zeros(10, dtype=int)],
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"cross-selector":["SelectC", np.zeros(7, dtype=int)],
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"crossover":["Crossover" , np.zeros(10, dtype=int)],
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"mutation-rate":["pm",np.zeros(10, dtype=int)],
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"mut-selector":["SelectM",np.zeros(7, dtype=int)],
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"mutation":["Mutation", np.zeros(11, dtype=int)],
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"replacement":["Replacement" ,np.zeros(11, dtype=int)]} for i in range(19)]
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else:
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res=[{"crossover-rate":["pc" , np.zeros(10, dtype=int)],
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"cross-selector":["SelectC", np.zeros(7, dtype=int)],
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"crossover":["Crossover" , np.zeros(10, dtype=int)],
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"mutation-rate":["pm",np.zeros(10, dtype=int)],
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"mut-selector":["SelectM",np.zeros(7, dtype=int)],
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"mutation":["Mutation", np.zeros(11, dtype=int)],
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"replacement":["Replacement" , np.zeros(11, dtype=int)],
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"pop-size":["pop-size", np.zeros(10, dtype=int)],
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"offspring-size":["offspring-size" , np.zeros(10, dtype=int)]} for i in range(19)]
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for fastgadir in os.listdir(os.path.join(path,"raw/data")): #fastgadir : directory of 50 runs of an elite configuration
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algo=fastgadir.split("_")
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pb=int(fastgadir.split("_")[0].split("=")[1])
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for param in algo[1:]:
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name,val=param.split("=")[0],float(param.split("=")[1])
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if(name in {"pop-size" ,"offspring-size"}):
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if(val%5==0):
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res[pb][name][1][int(val//5) -1]+=1
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else:
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#print(res[pb][name][1],val//5)
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res[pb][name][1][int(val//5)]+=1
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elif(name in {"crossover-rate","mutation-rate"} ):
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if(int(val*10)==10): #case of val=1
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res[pb][name][1][-1]+=1
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else :
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#print(int(float(val)*10), name,pb,val)
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res[pb][name][1][int(val*10)]+=1
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else :
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res[pb][name][1][int(val)]+=1
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for pb in range(19):
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name="distribution_pb="+str(pb)+"_"+path.split("/")[-2]+".csv" #the end of the path must be /
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with open(os.path.join(distdir,name),"w+") as csvfile:
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csvfile.write("Op index, "+",".join(map(str,range(0,11)))+"\n")
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with open(os.path.join(distdir,name),"a") as csvfile:
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for param_name in res[pb].keys():
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#print(map(str,res[ind]),res[ind], ",".join(map(str,res[ind])))
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csvfile.write(res[pb][param_name][0]+","+ ",".join(map(str,res[pb][param_name][1]))+",-"*(11-len(res[pb][param_name][1])) +"\n")
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#print(str(i)+",",",".join(map(str,np.mean(aucs[i],1))))
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#all problems
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name ="distribution_all_pb_"+path.split("/")[-1]+".csv"
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with open(os.path.join(path,"raw",name),'w+') as csvfile:
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csvfile.write("Op index, "+",".join(map(str,range(0,11)))+"\n")
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with open(os.path.join(path,"raw",name),'a') as csvfile:
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for param_name in res[0].keys():
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#print(map(str,res[ind]),res[ind], ",".join(map(str,res[ind])))
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csvfile.write(res[0][param_name][0]+","+ ",".join(map(str,np.sum([res[i][param_name][1] for i in range(19)],0)))+",-"*(11-len(res[0][param_name][1])) +"\n") #res[0] only for getting the name of parameters
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#print(str(i)+",",",".join(map(str,np.mean(aucs[i],1)))) |