140 lines
No EOL
6.8 KiB
Python
Executable file
140 lines
No EOL
6.8 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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from scipy.stats import mannwhitneyu
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##cmd eg :
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# python3 hist_by_2_4_5.py ./fastga_results_all/ ./hist_and_csv/ 100000 1000
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#get the Mann Whitney test U results between the plan F and plan R
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# (change ligne 23 and 44 for other plan, and the maxExp, maxEv for other budget)
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path=sys.argv[1]
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figpath=sys.argv[2] #directory to store the data
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maxExp=sys.argv[3]
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maxEv=sys.argv[4]
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hist_pb=[[] for i in range(19)]
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name=[]
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randind=-1
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for fastga in os.listdir(path): #ddir : directory of fastga_plan
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if(fastga in {"fastga_results_planF"}):
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for plan in os.listdir(os.path.join(path,fastga)):
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print("maxExp="+str(maxExp)+"_maxEv="+str(maxEv)+"_" in plan,plan,"maxExp="+str(maxExp)+"_maxEv="+str(maxEv))
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#print("maxExp="+str(maxExp)+"_maxEv="+str(maxEv) in plan,plan,"maxExp="+str(maxExp)+"_maxEv="+str(maxEv))
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if("maxExp="+str(maxExp)+"_maxEv="+str(maxEv)+"_" in plan):
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name.append("_".join(plan.split("_")[:3]))
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for fastgadir in os.listdir(os.path.join(path,fastga,plan,"raw","data")): #fastgadir : directory of 50 runs of a configuration
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pb=int(fastgadir.split("_")[0].split("=")[1])
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average_pb=[]
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for fname in os.listdir(os.path.join(path,fastga,plan,"raw","data",fastgadir)):
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with open(os.path.join(path,fastga,plan,"raw","data",fastgadir,fname)) as fd:
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auc = float(fd.readlines()[0])
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average_pb.append(auc)
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if(hist_pb[pb]==[]): #first algo
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hist_pb[pb].append(average_pb)
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elif(len(hist_pb[pb])!=len(name)):
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hist_pb[pb].append(average_pb)
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else:
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hist_pb[pb][len(name)-1]+=average_pb #another algo for the same plan
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if("random" in fastga):
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for randir in os.listdir(os.path.join(path,fastga)):
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#eg path: maxEv=100_nbAlgo=15_2021-08-20T1511+0200_results_randoms
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if(("maxEv="+str(maxEv)+"_") in randir):
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print("maxEv="+str(maxEv) in randir,randir)
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name.append(randir.split("_")[0]+"_random")
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randind=len(name)-1
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print(randind,name)
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for ddir in os.listdir(os.path.join(path,fastga,randir)): #ddir : directory of one run_elites_all or more
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if("crossover" in ddir):
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for fastgadir in os.listdir(os.path.join(path,fastga,randir,ddir,"data")): #fastgadir : directory of 50 runs of a configuration
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average_pb=[]
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pb=int(fastgadir.split("_")[0].split("=")[1])
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for fname in os.listdir(os.path.join(path,fastga,randir,ddir,"data",fastgadir)):
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with open(os.path.join(path,fastga,randir,ddir,"data",fastgadir,fname)) as fd:
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auc = float(fd.readlines()[0])
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average_pb.append(auc)
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#print(len(hist_pb[pb]),len(name), pb)
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if(hist_pb[pb]==[]): #first algo
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#print("entrer random vide")
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hist_pb[pb].append(average_pb)
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elif(len(hist_pb[pb])!=len(name)):
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#print("entrer random !=")
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hist_pb[pb].append(average_pb)
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else:
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hist_pb[pb][len(name)-1]+=average_pb #another algo for the same plan
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figdir=os.path.join(figpath,"mwtestU_FR")
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try:
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os.makedirs(figdir)
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except FileExistsError:
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pass
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#colors=['yellow', 'green',"blue","pink","purple","orange","magenta","gray","darkred","cyan","brown","olivedrab","thistle","stateblue"]
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print(name)
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filename="mwtestU_maxExp={}_maxEv={}_FR.csv".format(maxExp,maxEv)
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with open(os.path.join(figdir,filename),'w+') as csvfile:
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csvfile.write(" ,"+",".join(map(str,range(0,19)))+"\n")
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meanvalue=[]
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pvalue=[]
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meanR=[]
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meanF=[]
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mdianR=[]
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mdianF=[]
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mdianvalue=[]
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iqrR=[]
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iqrF=[]
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stdR=[]
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stdF=[]
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iqrvalue=[]
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pstd=[]
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for pb in range(19):
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#hR,lR,_=plt.hist(hist_pb[pb][randind],bins=10,range=(-1,0),align="mid",label=name) #no label color=colors[:len(name)]
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#hF,lF,_=plt.hist(hist_pb[pb][np.abs(1-randind)],bins=10,range=(-1,0),align="mid",label=name) #no label color=colors[:len(name)]
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_,pv=mannwhitneyu(hist_pb[pb][np.abs(1-randind)],hist_pb[pb][randind])
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print(_,pv)
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#meanvalue.append(np.mean(np.array(hF)*np.array(lF[:len(lF)-1]))-np.mean(np.array(hR)*np.array(lR[:len(lR)-1])))
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pstd.append(np.std(hist_pb[pb][np.abs(1-randind)])-np.std(hist_pb[pb][randind]))
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stdF.append(np.std(hist_pb[pb][np.abs(1-randind)]))
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stdR.append(np.std(hist_pb[pb][randind]))
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meanF.append(np.mean(hist_pb[pb][np.abs(1-randind)]))
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meanR.append(np.mean(hist_pb[pb][randind]))
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mdianF.append(np.median(hist_pb[pb][np.abs(1-randind)]))
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mdianR.append(np.median(hist_pb[pb][randind]))
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mdianvalue.append(np.median(hist_pb[pb][np.abs(1-randind)])-np.median(hist_pb[pb][randind]))
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meanvalue.append(np.mean(hist_pb[pb][np.abs(1-randind)])-np.mean(hist_pb[pb][randind]))
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pvalue.append(pv)
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Q1 = np.percentile(hist_pb[pb][np.abs(1-randind)], 25, interpolation = 'midpoint')
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# Third quartile (Q3)
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Q3 = np.percentile(hist_pb[pb][np.abs(1-randind)], 75, interpolation = 'midpoint')
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# Interquaritle range (IQR)
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iqrF.append( Q3 - Q1)
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Q1 = np.percentile(hist_pb[pb][randind], 25, interpolation = 'midpoint')
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# Third quartile (Q3)
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Q3 = np.percentile(hist_pb[pb][randind], 75, interpolation = 'midpoint')
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# Interquaritle range (IQR)
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iqrR.append( Q3 - Q1)
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print(_,pv)
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iqrvalue=np.array(iqrF)-np.array(iqrR)
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with open(os.path.join(figdir,filename),'a') as csvfile:
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csvfile.write("mF-mR,"+",".join(map(str,meanvalue))+"\n")
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csvfile.write("p_value,"+",".join(map(str,pvalue))+"\n")
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csvfile.write("mF,"+",".join(map(str,meanF))+"\n")
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csvfile.write("mR,"+",".join(map(str,meanR))+"\n")
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csvfile.write("medianF-medianR,"+",".join(map(str,mdianvalue))+"\n")
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csvfile.write("medianF,"+",".join(map(str,mdianF))+"\n")
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csvfile.write("medianR,"+",".join(map(str,mdianR))+"\n")
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csvfile.write("stdF-stdR,"+",".join(map(str,mdianvalue))+"\n")
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csvfile.write("stdF,"+",".join(map(str,stdF))+"\n")
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csvfile.write("stdR,"+",".join(map(str,stdR))+"\n")
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csvfile.write("iqrF,"+",".join(map(str,iqrF))+"\n")
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csvfile.write("iqrR,"+",".join(map(str,iqrR))+"\n")
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csvfile.write("iqrF-iqrR,"+",".join(map(str,iqrvalue))+"\n")
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