def better(new,old):
"better if (1)less median or (2)same and less iqr"
t = The.misc.a12
betterIqr = new.has().iqr < old.has().iqr
if new.lessp:
betterMed = new.has().median >= old.has().median
same = a12(old._cache, new._cache) <= t
else:
betterMed = new.has().median <= old.has().median
same = a12(new._cache, old._cache) <= t
return betterMed, same, betterIqr
def effect_size(sapienzdiv_data, sapienz_data, property, app):
# max obj., then rev true
# min obj., then rev false
if property == ids.UNIQUE_CRASHES or property == ids.MAX_COVERAGE:
rev = True
elif property == ids.AVG_MIN_LENGTH or property == ids.EXEC_TIME:
rev = False
# use effect size code by Tim
es_tim = a12(sapienzdiv_data, sapienz_data, rev)
es_tim_round = round(es_tim, 2)
# compute with alternative code
if len(sapienzdiv_data) == len(sapienz_data):
if rev:
es_alt = VD_A(sapienzdiv_data, sapienz_data)
else:
es_alt = VD_A(sapienz_data, sapienzdiv_data)
es_alt_round = round(es_alt[0], 2)
else:
es_alt_round = "N/A"
print("Effect size SAPIENZ-SAPIENZDIV for property " + property + ": " + str(es_tim_round) + " / " + str(es_alt_round) + "\t(app: " + str(app) + ")")
return es_tim_round
def joes_stats_reporter(problems, algorithms, tag=""):
#folder prefix for storing reports
date_folder_prefix = strftime("%m-%d-%Y")
#if folder does not exist, create it
if not os.path.isdir('reports/' + date_folder_prefix):
os.makedirs('reports/' + date_folder_prefix)
#fignum generator counts the number of files in the folder
fignum = len([name for name in os.listdir('reports/' + date_folder_prefix)]) + 1
#optional tag name for the file
fa = open('reports/' + date_folder_prefix + "/stats_suite_summary_report" + "_" + tag + str("%02d" % fignum) + ".txt", 'w')
#container for summary data
data = []
algranks = [[] for alg in algorithms]
nemalgranks = [[] for alg in algorithms]
for p,problem in enumerate(problems):
data.append([])
reports = []
for a,algorithm in enumerate(algorithms):
filename = DATA_PREFIX + SUMMARY_RESULTS+problem.name + "-p" + str(MU) + "-d" + str(len(problem.decisions)) + "-o" + str(len(problem.objectives))+"_"+algorithm.name+DATA_SUFFIX
print filename
exit()
finput = open(filename, 'rb')
reader = csv.reader(finput, delimiter=',')
data[p].append([])
for i,row in enumerate(reader):
if i == 0:
data[p][a] = [[] for obj in problem.objectives] + [[],[],[],[]]
elements = row
data[p][a][0].append( int(elements[3]) )
for o,obj in enumerate(problem.objectives):
data[p][a][1+o].append( float(elements[4+o]) )
data[p][a][1+len(problem.objectives)].append( float(elements[4+len(problem.objectives)]) )
data[p][a][2+len(problem.objectives)].append( float(elements[5+len(problem.objectives)]) )
data[p][a][3+len(problem.objectives)].append( float(elements[6+len(problem.objectives)]) )
s = '{0: <12}'.format(problem.name + "-p" + str(MU) + "-d" + str(len(problem.decisions)) + "-o" + str(len(problem.objectives))) + "," + '{0: <12}'.format(algorithm.name) + ","
s += str(avg(data[p][a][0])) + ","
for dug in data[p][a]:#[len(problem.objectives)+1:]:
s += str("%10.2f" % avg(dug)) + ","
reports.append( s )
#read baseline
filename = "data/" + problem.name + "-p" + str(MU) + "-d" + str(len(problem.decisions)) + "-o" + str(len(problem.objectives)) + "-dataset.txt"
f2input = open(filename, 'rb')
reader2 = csv.reader(f2input, delimiter=',')
s = '{0: <12}'.format(problem.name) + "," + '{0: <12}'.format("Baseline") + "," + str("%10.2f" % 0) + ",0,"
for i,row in enumerate(reader2):
if i > MU:
s += str("%10.2f" % float(row[1])) + ","
s += str("%10.2f" % 1) + "," + str("%10.2f" % 0)
print s
fa.write(s + "\n")
IBDs = [data[p][a][1+len(problem.objectives)] for a in range(len(algorithms))]
AVGs = [avg(IBDs[a]) for a in range(len(algorithms))]
#print avg(IBDs[0]), avg(IBDs[1]), avg(IBDs[2])
groups = IBDs
num_groups = len(algorithms)
num_blocks = repeats
cd = critical_difference(0.01, num_groups, num_blocks)
#normed_data = [ [(g - avg(group))/(var(group)**2) for g in group] for group in groups]
#rv = [stats.kstest(nd, 'norm') for nd in normed_data]
#print "We can reject that the data is normal: ", [r[1]<0.01 for r in rv]
ranks = []
for i in range(len(groups[0])):
array = numpy.array([group[i] for group in groups])
ranks.append( stats.rankdata(array) )
rankcols = [[rank[i] for rank in ranks] for i in range(len(groups))]
rankavgs = [avg(rankcols[i]) for i in range(len(groups))]
z1,p1 = stats.ranksums(IBDs[0], IBDs[1])
z2,p2 = stats.ranksums(IBDs[0], IBDs[2])
z3,p3 = stats.ranksums(IBDs[1], IBDs[2])
a1= a12(IBDs[0], IBDs[1])
a2= a12(IBDs[0], IBDs[2])
a3= a12(IBDs[1], IBDs[2])
a12mat = []
#print "A12 Effect Size Matrix"
#print '{0: <12}'.format("" ), '{0: >12}'.format("NSGAII"), '{0: >12}'.format("GALE"), '{0: >12}'.format("SPEA2")
#print '{0: <12}'.format("NSGAII"),
a12mat.append('{0: >12}'.format("-" ) + "," + str("%12.2f" % a1) + "," + str("%12.2f" % a2))
#print '{0: <12}'.format("GALE" ),
a12mat.append(str("%12.2f" % (1-a1)) + "," + '{0: >12}'.format("-" ) +"," + str("%12.2f" % a3))
#print '{0: <12}'.format("SPEA2" )
a12mat.append(str("%12.2f" % (1-a2)) + "," + str("%12.2f" % (1-a3)) +"," + '{0: >12}'.format("-"))
alpha = 0.05
wins = [0 for a in range(len(algorithms))]
nemwins = [0 for a in range(len(algorithms))]
if p1 < alpha:
"gale vs nsgaii are different"
if AVGs[0] < AVGs[1]:
"gale wins over nsgaii"
wins[0] += 1
else:
wins[1] += 1
if p2 < alpha:
"gale vs spea2 are different"
if AVGs[0] < AVGs[2]:
"gale wins over spea2"
wins[0] += 1
else:
wins[2] += 1
if p3 < alpha:
"nsgaii vs spea2 are different"
if AVGs[1] < AVGs[2]:
wins[1] += 1
else:
wins[2] += 1
#nemenyi ranking
if (abs(rankavgs[0]-rankavgs[1])) > cd:
"gale vs nsgaii are different"
if rankavgs[0] < rankavgs[1]:
"gale wins over nsgaii"
nemwins[0] += 1
else:
nemwins[1] += 1
if (abs(rankavgs[0]-rankavgs[2])) > cd:
"gale vs spea2 are different"
if rankavgs[0] < rankavgs[2]:
"gale wins over spea2"
nemwins[0] += 1
else:
nemwins[2] += 1
if (abs(rankavgs[1]-rankavgs[2])) > cd:
"nsgaii vs spea2 are different"
if rankavgs[1] < rankavgs[2]:
nemwins[1] += 1
else:
nemwins[2] += 1
# rank algorithms by wins
names = [alg.name for alg in algorithms]
ranks = {}
for index,w in enumerate(wins):
if w in ranks: ranks[w].append(names[index])
else: ranks[w] = [names[index]]
reversed_keys = sorted(ranks.keys(), reverse=True)
#print [str(ranks[r]) + "#" + str(rk+1) for rk,r in enumerate(reversed_keys)]
# rank algorithms by nemwins
names = [alg.name for alg in algorithms]
nemranks = {}
for index,w in enumerate(wins):
if w in nemranks: nemranks[w].append(names[index])
else: nemranks[w] = [names[index]]
reversed_keys = sorted(nemranks.keys(), reverse=True)
rank_reports = ["" for alg in algorithms]
for rk,r in enumerate(reversed_keys):
for item in ranks[r]:
for what,name in enumerate(names):
if item == name: it = what
algranks[it].append(rk+1)
rank_reports[it] = "Rank" + ": #" + str(rk+1)
nemrank_reports = ["" for alg in algorithms]
for rk,r in enumerate(reversed_keys):
for item in nemranks[r]:
for what,name in enumerate(names):
if item == name: it = what
nemalgranks[it].append(rk+1)
nemrank_reports[it] = "nemRank" + ": #" + str(rk+1)
nemenyi = []
for ra in rankavgs:
nemenyi.append( str(ra) + "," + str(cd) )
for dig,dug,dag,dog,deg in zip(reports, rank_reports, a12mat, nemenyi,nemrank_reports):
print dig, dug, "," , dag, ", ", deg
fa.write(dig + dug + dag + dog + deg + "\n")
print "===Average Rank==="
fa.write("===Average Rank===\n")
for rk,alg in zip(algranks, algorithms):
print alg.name + ":" + str("%12.2f" % avg(rk))
fa.write(alg.name + ":" + str("%12.2f" % avg(rk)) + "\n")
print "===Average nemRank==="
fa.write("===Average nemRank===\n")
for rk,alg in zip(nemalgranks, algorithms):
print alg.name + ":" + str("%12.2f" % avg(rk))
fa.write(alg.name + ":" + str("%12.2f" % avg(rk)) + "\n")
fa.close()