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SEDE_RQ3.py
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SEDE_RQ3.py
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import HeatmapModule
import retrainModule
from imports import torch, join, os, stat, setupTransformer, PathImageFolder, sc, pd, np, plt, random
import searchModule, assignModule
def generateO1(caseFile):
layerClust = join(caseFile["filesPath"], "ClusterAnalysis_" + str(caseFile["clustMode"]),
caseFile["selectedLayer"] + ".pt")
clsData = torch.load(layerClust, map_location=torch.device('cpu'))
print("Loading HM distance file for the selected layer.")
HMDistFile = join(caseFile["filesPath"], caseFile["selectedLayer"] + "HMDistance.xlsx")
clusterRadius, centroidHM, testHM = assignModule.getClusterData(caseFile, HMDistFile)
searchModule.search(caseFile, clsData, centroidHM, clusterRadius, [15], [30])
return
def plotRQ(caseFile):
layerClust = join(caseFile["filesPath"], "ClusterAnalysis_" + str(caseFile["clustMode"]),
caseFile["selectedLayer"] + ".pt")
clsData = torch.load(layerClust, map_location=torch.device('cpu'))
RQ3(clsData['clusters'], caseFile)
return
def RQ3(clusters, caseFile):
print("IEE Simulator V", caseFile["iee_version"])
print("SEDE Evaluation based on simulator parameters reduction")
dictVar = {}
SEDE_imgs = {}
GI = join(caseFile["filesPath"], "GeneratedImages")
outPath = join(caseFile["filesPath"], "Pool")
caseFile["SimDataPath"] = outPath
if int(caseFile["iee_version"]) == 2:
paramList, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _ = searchModule.getParamVals_2()
else:
paramList, _, _, _, _, _, _, _, _, _, _ = searchModule.getParamVals()
for param in paramList:
dictVar[param] = []
for ID in clusters:
SEDE_imgs[str(ID)] = []
#path = join(GI, str(ID-7))
path = join(GI, str(ID))
#res = searchModule.loadCP(join(path, 'res1.pop'))
imgs = []
dirPath = join(path, 'Pop', '1')
dir = os.listdir(join(path, 'Pop', '1'))
res = searchModule.loadCP(join(path, "res1.pop"))
if hasattr(res, "pop"):
res = res.pop
for ind in res:
x = ind.X
img = searchModule.processX(x)
#imgPath = join(outPath, img + ".png") #fixme
imgPath = join(dirPath, img + ".png") #fixme
#print(imgPath)
imgs.append(img.split(".png")[0])
x = []
imgParams = (img.split(".png")[0]).split("_")
for j in imgParams:
x.append(float(j))
if len(x) == searchModule.nVar:
SEDE_imgs[str(ID)].append(imgPath)
i = 0
for param in paramList:
for img in imgs:
x = []
imgParams = (img.split(".png")[0]).split("_")
#print(imgParams)
for j in imgParams:
x.append(float(j))
if len(x) > searchModule.nVar:
continue
dictVar[param].append(x[i])
i += 1
print("Collected all O1 data")
dictVar_2 = {}
dictLabel = {}
for ID in clusters:
print(ID)
dictLabel[str(ID)] = []
dictVar_2[str(ID)] = {}
#path = join(GI, str(ID-7))
path = join(GI, str(ID))
dirPath = join(path, 'Pop', '1')
#res = searchModule.loadCP(join(path, 'res1.pop'))
imgs = []
dir = os.listdir(join(path, 'Pop', '1'))
res = searchModule.loadCP(join(path, "res1.pop"))
if hasattr(res, "pop"):
res = res.pop
for ind in res:
x = ind.X
img = searchModule.processX(x)
imgPath = join(outPath, img + ".png") #fixme
imgPath = join(dirPath, img + ".png") #fixme
#print(imgPath)
x = []
imgParams = (img.split(".png")[0]).split("_")
for j in imgParams:
x.append(float(j))
if len(x) > searchModule.nVar:
continue
imgs.append(imgPath)
#if not os.path.isfile(imgPath):
# if int(caseFile["iee_version"]) == 2:
# searchModule.generateHuman(x, caseFile)
# else:
# searchModule.generateAnImage(x, caseFile)
#N, DNNResult, P, L, D3, layersHM = searchModule.doImage(imgPath, caseFile, None)
#dictLabel[str(ID)].append(L)
i = 0
for param in paramList:
dictVar_2[str(ID)][param] = []
for img in imgs:
x = []
imgParams = (os.path.basename(img).split(".png")[0]).split("_")
for j in imgParams:
x.append(float(j))
if len(x) > searchModule.nVar:
continue
dictVar_2[str(ID)][param].append(x[i]) #TODO: check if correct params
i += 1
resDict = {}
count1 = 0
count2 = 0
count3 = 0
count4 = 0
count5 = 0
#vals = [[0.0] * 10] * 23
vals = []
for j in range(0, searchModule.nVar):
vals.append([])
for i in range(0, len(clusters)):
vals[j].append(0.0)
#print(vals)
i = 0
for ID in clusters:
continue
resDict[str(ID)] = []
j = 0
for param in dictVar_2[str(ID)]:
varRed = stat.variance(dictVar_2[str(ID)][param])/stat.variance(dictVar[param])
varRedPerc = 100*(1-varRed)
varRedPerc = 0 if varRedPerc < 0 else varRedPerc
#print(j, i)
vals[j][i] = varRedPerc
#if 0.0 <= varRed <= 0.1:
# count5 += 1
# break
#if 0.1 < varRed <= 0.2:
# count4 += 1
# break
#if 0.2 < varRed <= 0.3:
# count3 += 1
# break
#if 0.3 < varRed <= 0.4:
# count2 += 1
# break
#if 0.4 < varRed <= 0.5:
# count1 += 1
# break
#print(ID, param, 100*(1-varRed), max(dictVar_2[str(ID)][param]), min(dictVar_2[str(ID)][param]), sum(dictVar_2[str(ID)][param])/len(dictVar_2[str(ID)][param]))
#print(len(dictVar_2[str(ID)][param]), len(dictVar[param]))
#print(dictVar_2[str(ID)][param])
#print(dictVar[param])
j += 1
i += 1
#print(vals[0])
coloumnList = []
#clusters = [1, 2, 4, 5, 6, 8, 10] #HPD-F
#clusters = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
clusters = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] #HPD-H
for ID in clusters:
coloumnList.append('RCC-'+str(int(ID)))
df = pd.DataFrame(vals, columns=coloumnList)
ax = df.plot.box(grid='True')
ax.set_ylabel('Variance Reduction (%)')
figure = (ax).get_figure()
# figure.savefig(join(GI_path, "RQ4.png"))
figure.savefig(join(GI, "RQ3-HPD2.png"))
# figure.savefig(join(GI_path, "RQ4.pdf"))
spread = np.random.rand(50) * 100
center = np.ones(25) * 50
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
data = np.concatenate((spread, center, flier_high, flier_low))
fig1, ax1 = plt.subplots()
ax1.set_title('Basic Plot')
ax1.boxplot(data)
#print(vals)
print(count1, count2, count3, count4, count5)
return dictLabel, SEDE_imgs