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RQ1.py
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#
# Copyright (c) University of Luxembourg 2019-2020.
# Created by Hazem FAHMY, hazem.fahmy@uni.lu, SNT, 2019.
#
import paramsModule as pM
from imports import np, os, math, sc, stat, pd, torch, Workbook, dataframe_to_rows, join, basename
def UnityRQ1(caseFile):
normalizedValues = [0.06, 0.11, 0.19, 0.32, 0.32,0.43,0.44,0.47,0.50,0.51,0.56,0.56,0.69,0.74,0.82,0.88,0.94,0.94]
#normalizedValues = [0.06,0.07,0.19,0.26,0.32,0.44,0.48,0.56,0.58,0.60,0.69,0.74,0.82,0.93,0.94]
DataSetsPath = caseFile["DataSetsPath"]
datasetName = caseFile["datasetName"]
xlPath = join(caseFile["filesPath"], "Layer0HMDistance.xlsx")
outExcelPath = join(caseFile["outputPath"], caseFile["datasetName"] + "_RQ1.xlsx")
clustersPath = join(caseFile["outputPath"], "ClusterAnalysis_" + caseFile["clustMode"])
layers = caseFile["selectedLayer"]
ClusterOf = {}
N_clusters = len(normalizedValues)
for C in range(0, N_clusters):
ClusterOf[C] = C
clusterCOUNTER = N_clusters
for D in range(0, 11):
for S in range(1, N_clusters):
for P in range(0, N_clusters):
if (P+S) >= N_clusters:
break
merge = False
if float(normalizedValues[P + S] - normalizedValues[P]) <= float(D/100):
merge = True
if P > 0:
if ClusterOf[P-1] == ClusterOf[P]:
merge = False
if P + S < (N_clusters-1):
if ClusterOf[P+S] == ClusterOf[P+S+1]:
merge = False
if merge:
clusterCOUNTER += 1
for Element in range(P, P+S+1):
#print(Element, clusterCOUNTER)
ClusterOf[Element] = clusterCOUNTER
print(ClusterOf)
Train_dir = join(DataSetsPath, "TrainingSet")
Train_json = join(DataSetsPath, "TrainingSet_json")
Test_dir = join(DataSetsPath, "TestSet")
Test_json = join(DataSetsPath, "TestSet_json")
Set = list()
xlFile = pd.read_excel(xlPath)
i = 0
for col in xlFile.columns:
if i == 0:
i = 1
else:
Set.append(col)
labelList = list()
AvgICDList = list()
SilList = list()
newAvgICDList = list()
for layer in layers:
clusters = torch.load(join(str(clustersPath), layer + ".pt"))
labelListed = clusters['label list']
labelList.append([(i) for i in labelListed])
AvgICDList.append(clusters['avgLayer'])
newAvgICDList.append(clusters['avgLayer'])
SilList.append(clusters['silhouette'])
excel(labelList, AvgICDList, SilList, newAvgICDList, Set, Train_dir, Test_dir, Train_json, Test_json,
datasetName, outExcelPath, layers)
def excel(cc, AvgICDList, SilList, newAvgICDList, FullTestSet, Train_dir, Test_dir, Train_json, Test_json, mode, clustexcel, layers):
arr = [0] * len(cc)
wb = Workbook()
wb.save(clustexcel)
red = [0] * (11)
discarded = [0] * (11)
i = 0
for layer in layers:
df1, df2, df3, arr[i], df4, discarded[i], red[i] = pM.excelsheet(cc[i], FullTestSet, Train_dir, Test_dir, Train_json, Test_json,
mode)
sheet = wb.create_sheet(str(layer), 0)
for x in dataframe_to_rows(df1):
sheet.append(x)
sheet2 = wb.create_sheet('Summary_' + str(layer), 0)
for x in dataframe_to_rows(df2):
sheet2.append(x)
sheet3 = wb.create_sheet('Thresholds' + str(layer), 0)
z = 0
for x in dataframe_to_rows(df3):
if (z != 1):
sheet3.append(x)
z = z + 1
i = i + 1
labels = ["Layer#", "Cluster#", "AvgDisp_Var%_All", "AvgDisp_Var%_1", "AvgDisp_Var%_3", "AvgDisp_Var%_4", "AvgDisp_Var%_Highest50%+", "Pass#_Angle",
"Pass#_HHeadpose", "Pass#_VHeadpose", "Pass#_StrangeDist", "Pass#_Dist", "LayerScore1", "LayerScore2",
"NumClusters_Had_Below50", "NumClusters_Had_Below50_CrossingBoundary", "%ClstBelow50", "%ClstBelowCross"]
i = 0
for layer in layers:
arr[i] = [layer] + arr[i]
i = i + 1
col = [0] * len(labels)
for i in range(0, len(col)):
col[i] = [0] * len(arr)
for i in range(0, len(labels)):
for j in range(0, len(arr)):
col[i][j] = arr[j][i]
df4[labels[i]] = col[i]
ChosenLayers = ['']*4
ChosenLayers[0] = bestlayer(red, min(red), layers)
ChosenLayers[1] = bestlayer(AvgICDList, min(AvgICDList), layers)
ChosenLayers[2] = bestlayer(SilList, max(SilList), layers)
ChosenLayers[3] = bestlayer(newAvgICDList, min(newAvgICDList), layers)
df4["# Discarded Clusters"] = discarded
df4["AvgPass_Angle"] = [i / j for i, j in zip(col[6], col[1])]
df4["AvgPass_HHeadpose"] = [i / j for i, j in zip(col[7], col[1])]
df4["AvgPass_VHeadpose"] = [i / j for i, j in zip(col[8], col[1])]
df4["AvgPass_StrangeDist"] = [i / j for i, j in zip(col[9], col[1])]
df4["AvgPass_Dist"] = [i / j for i, j in zip(col[10], col[1])]
df4["Avg Reduction in Variance [VarP[clusters]/(n_clusters-discarded)]"] = red
#df4["avg1"]
#df4["avg3"]
#df4["avg4"]
#df4["avgall"]
#df4["avgallbelow50"]
#df4["avg3below50"]
df4["AvgDist_Per_DataPoint"] = AvgICDList
df4["Silhouette"] = SilList
df4["NewAvgICD"] = newAvgICDList
df4["AvgRed/AvgICD/Silh/newAvgICD"] = pd.Series(ChosenLayers)
sheet4 = wb.create_sheet("Summary", 0)
i = 0
for x in dataframe_to_rows(df4):
if(i != 1):
sheet4.append(x)
i = i+1
wb.save(clustexcel)
def bestlayer(avgList, minAvg, layers):
minIndex = [i for i, j in enumerate(avgList) if j == minAvg]
minIndex = minIndex[0]
minLayer = layers[minIndex]
return minLayer
def getDetails(VarRedC, DictList):
#print(100 - (VarRedC * 100), sum(DictList)/len(DictList), min(DictList), max(DictList), stat.variance(DictList),
# stat.median(DictList), stat.stdev(DictList), np.percentile(DictList, 25), np.percentile(DictList, 75))
#print(DictList)
return [100 - (VarRedC * 100), sum(DictList)/len(DictList), min(DictList), max(DictList), stat.variance(DictList),
stat.median(DictList), stat.stdev(DictList), np.percentile(DictList, 25), np.percentile(DictList, 75)]
def IEERQ1(caseFile):
faceSubset = caseFile["faceSubset"] # Face component
testNPY = caseFile["testDataNpy"] # testdataset npy file
trainNPY = caseFile["trainDataNpy"] # traindataset npy
trainCSV = caseFile["trainCSV"] # csv file - train
trainCSV = None # csv file - train
testCSV = caseFile["testCSV"] # csv file - test
layers = caseFile["layers"] ##
Alex = caseFile["Alex"] ## HPD
FLD = caseFile["FLD"] ## FLD
clustMode = caseFile["clustMode"]
outputPath = caseFile["outputPath"]
dataset1 = np.load(trainNPY, allow_pickle=True)
dataset1 = dataset1.item()
x_data = dataset1["label"]
x_config = dataset1["config"]
clustersData = {}
if not Alex:
clustexcel = join(outputPath, faceSubset + "_RQ1.xlsx")
else:
clustexcel = join(outputPath, "RQ1.xlsx")
writer = pd.ExcelWriter(clustexcel, engine='xlsxwriter')
arr = list()
counter = 0
for z in x_config[0]:
print(z)
print(x_config[0][z])
if isinstance(x_config[0][z], str):
#print(x_config[0][z])
counter = counter
elif isinstance(x_config[0][z], float):
#print(x_config[0][z])
arr.append(z)
counter += 1
elif len(x_config[0][z]) == 1:
arr.append(z)
#print(x_config[0][z])
counter += 1
else:
i = 0
for h in x_config[0][z]:
arr.append(z + str(i))
#print(z + str(i), h)
i += 1
counter += 1
dataset2 = np.load(testNPY, allow_pickle=True)
dataset2 = dataset2.item()
y_data = dataset2["label"]
y_config = dataset2["config"]
totalClustersList = list()
PercOfRedClustersList = list()
HighestVarRedOverClustersList = list()
AvgVarDispList = list()
layers = [caseFile["selectedLayer"]] # ["clustersFile"] -- clustersFile.pt # {'clusters': 1: 'members': X,Y, 2: 'members':}
layers = ["Layer15"] # ["clustersFile"] -- clustersFile.pt # {'clusters': 1: 'members': X,Y, 2: 'members':}
for layerX in layers:
#layerClust = join(caseFile["outputPath"], caseFile["RCC"], "ClusterAnalysis_" + str(clustMode), layerX + ".pt")
layerClust = join(caseFile["outputPath"], caseFile["RCC"], "ClusterAnalysis_" + str(clustMode), layerX + ".pt")
clsData = torch.load(layerClust, map_location=torch.device('cpu'))
clusterDictX = {}
clusterDictY = {}
clusterDictP = {}
clusterDictC = {}
allDictX = {}
allDictY = {}
allDictP = {}
allDictC = {}
VarRedXDict = {}
VarRedYDict = {}
VarRedPDict = {}
VarRedCDict = {}
for i in range(0, counter):
allDictC[i] = []
for i in range(0, 27):
allDictX[i] = []
allDictY[i] = []
allDictP[i] = []
for clusterID in clsData['clusters']:
if len(clsData['clusters'][clusterID]['members']) > 1:
groupMembers = clsData['clusters'][clusterID]['members']
clusterDictX[clusterID] = {}
clusterDictY[clusterID] = {}
clusterDictP[clusterID] = {}
clusterDictC[clusterID] = {}
for i in range(0, 27):
clusterDictX[clusterID][i] = []
clusterDictY[clusterID][i] = []
clusterDictP[clusterID][i] = []
for i in range(0, counter):
clusterDictC[clusterID][i] = []
if trainCSV is not None:
imageList = pd.read_csv(trainCSV)
for index, row in imageList.iterrows():
if row["result"] == "Wrong":
fileName = None
dataIndex = None
if not Alex:
if row["worst_component"] == faceSubset:
fileName = "Train_" + row["image"].split(".png")[0]
dataIndex = index
else:
fileName = "Train_" + str(basename(row["image"])).split(".png")[0] + "_" + row["expected"]
dataIndex = int(str(basename(row["image"])).split(".png")[0]) - 1
if groupMembers.count(fileName)>0:
labels = x_data[dataIndex]
x = 0
for KP in labels:
clusterDictX[clusterID][x].append(KP[0])
allDictX[x].append(KP[0])
clusterDictY[clusterID][x].append(KP[1])
allDictY[x].append(KP[1])
Z = KP[0]**2 + KP[1]**2
clusterDictP[clusterID][x].append(math.sqrt(Z))
allDictP[x].append(math.sqrt(Z))
x += 1
x = 0
params = x_config[dataIndex]
for param in params:
if isinstance(params[param], str):
#clusterDictC[clusterID][x].append(params[param])
#allDictC[x].append(params[param])
x = x
elif isinstance(params[param], float):
clusterDictC[clusterID][x].append(float(params[param]))
allDictC[x].append(float(params[param]))
x += 1
elif len(params[param]) == 1:
clusterDictC[clusterID][x].append(float(params[param]))
allDictC[x].append(float(params[param]))
x += 1
else:
i = 0
for parami in params[param]:
clusterDictC[clusterID][x].append(float(parami))
allDictC[x].append(float(parami))
#print(param + str(i), parami)
i += 1
x += 1
imageList = pd.read_csv(testCSV)
for index, row in imageList.iterrows():
if row["result"] == "Wrong":
fileName = None
dataIndex = None
if not Alex:
if row["worst_component"] == faceSubset:
fileName = "Test_" + row["image"].split(".png")[0]
dataIndex = index
else:
fileName = "Test_" + str(basename(row["image"])).split(".png")[0] + "_" + row["expected"]
dataIndex = int(str(basename(row["image"])).split(".png")[0]) - 1
if groupMembers.count(fileName)>0:
labels = y_data[dataIndex]
x = 0
for KP in labels:
clusterDictX[clusterID][x].append(KP[0])
allDictX[x].append(KP[0])
clusterDictY[clusterID][x].append(KP[1])
allDictY[x].append(KP[1])
Z = KP[0]**2 + KP[1]**2
clusterDictP[clusterID][x].append(math.sqrt(Z))
allDictP[x].append(math.sqrt(Z))
x += 1
params = y_config[dataIndex]
x = 0
for param in params:
if isinstance(params[param], str):
#clusterDictC[clusterID][x].append(params[param])
#allDictC[x].append(params[param])
x = x
elif isinstance(params[param], float):
clusterDictC[clusterID][x].append(float(params[param]))
allDictC[x].append(float(params[param]))
x += 1
elif len(params[param]) == 1:
clusterDictC[clusterID][x].append(float(params[param]))
allDictC[x].append(float(params[param]))
x += 1
else:
i = 0
for parami in params[param]:
clusterDictC[clusterID][x].append(float(parami))
allDictC[x].append(float(parami))
#print(param + str(i), parami)
i += 1
x += 1
print("Layer: ", layerX)
AvgVarDictX = {}
AvgVarDictY = {}
AvgVarDictP = {}
AvgVarDictC = {}
df3 = pd.DataFrame()
for KP in range(0, 27):
AvgVarDictX[KP] = []
AvgVarDictY[KP] = []
AvgVarDictP[KP] = []
for j in range(0, counter):
AvgVarDictC[j] = []
RedClusters = 0
totalClusters = 0
HighestVarRedOverClusters = list()
for clusterID in clsData['clusters']:
print("CLUSTER:", clusterID)
clustersData[clusterID] = {}
if len(clsData['clusters'][clusterID]['members']) > 1:
totalClusters += 1
HighVarRed = list()
check = 0
VarRedXDict[clusterID] = {}
VarRedYDict[clusterID] = {}
VarRedPDict[clusterID] = {}
VarRedCDict[clusterID] = {}
highestX = 0.5
highestY = 0.5
highestP = 0.5
highestC = 0.5
#for KP in range(0, 27):
#if (len(clusterDictX[clusterID][KP]) > 1) and (len(allDictX[KP]) > 1):
#VarRedX = stat.variance(clusterDictX[clusterID][KP])/stat.variance(allDictX[KP])
#VarRedY = stat.variance(clusterDictY[clusterID][KP])/stat.variance(allDictY[KP])
#VarRedP = stat.variance(clusterDictP[clusterID][KP])/stat.variance(allDictP[KP])
#AvgVarDictX[KP].append(VarRedX)
#AvgVarDictY[KP].append(VarRedY)
#AvgVarDictP[KP].append(VarRedP)
#if VarRedX < highestX:
# VarRedXDict[clusterID]["KP"+str(KP)] = getDetails(VarRedX, clusterDictX[clusterID][KP])
#if VarRedP < highestY:
# VarRedYDict[clusterID]["KP"+str(KP)] = getDetails(VarRedY, clusterDictY[clusterID][KP])
#if VarRedP < highestP:
# VarRedPDict[clusterID]["KP"+str(KP)] = getDetails(VarRedP, clusterDictP[clusterID][KP])
#HighVarRed.append(VarRedX)
#HighVarRed.append(VarRedY)
#HighVarRed.append(VarRedP)
VarRedCDict[clusterID] = {}
for j in range(0, counter):
if (len(clusterDictC[clusterID][j]) > 1) and (len(allDictC[j]) > 1):
if (stat.variance(allDictC[j]) == 0.0):
VarRedC = 1.0
else:
VarRedC = stat.variance(clusterDictC[clusterID][j])/stat.variance(allDictC[j])
AvgVarDictC[j].append(VarRedC)
HighVarRed.append(VarRedC)
#HighVarRed.append(VarRedC)
#HighVarRed.append(VarRedC)
#if VarRedC < highestC:
# VarRedCDict[clusterID][str(arr[j])] = getDetails(VarRedC, clusterDictC[clusterID][j])
if VarRedC < 0.5:
#if arr[j].startswith("head"):
#print(arr[j])
#print("Min in Cluster", min(clusterDictC[clusterID][j]))
#print("Max in Cluster", max(clusterDictC[clusterID][j]))
#print("Avg in Cluster", sum(clusterDictC[clusterID][j])/len(clusterDictC[clusterID][j]))
#print("Min", min(allDictC[j]))
#print("Max", max(allDictC[j]))
#print("Avg", sum(allDictC[j])/len(allDictC[j]))
#print(allDictC[j])
VarRedCDict[clusterID][str(arr[j])] = getDetails(VarRedC, clusterDictC[clusterID][j])
#print("1st Quartile:", VarRedCDict[clusterID][str(arr[j])][len(VarRedCDict[clusterID][str(arr[j])])-1])
#print("3rd Quartile:", VarRedCDict[clusterID][str(arr[j])][len(VarRedCDict[clusterID][str(arr[j])])-2])
#number of clusters had high variance reduction below 50% and unsafe
NumParams = 1
print("num params", NumParams)
#if FLD == 1:
# NumParams = 3
#elif FLD == 2:
# NumParams = 4
#elif FLD == 0:
# NumParams = 4
check = 0
check1 = 0
check2 = 0
check3 = 0
check4 = 0
check5 = 0
check6 = 0
check7 = 0
check8 = 0
check9 = 0
check10 = 0
for i in range(0, NumParams):
print("Highest Var Red", 100*(1-min(HighVarRed)))
HighestVarRed = min(HighVarRed)
HighestVarRedOverClusters.append(HighestVarRed)
if HighestVarRed < 0.10:
check1 += 1
if HighestVarRed < 0.20:
check2 += 1
if HighestVarRed < 0.30:
check3 += 1
if HighestVarRed < 0.40:
check4 += 1
print(HighestVarRed)
if HighestVarRed < 0.50:
check5 += 1
check += 1
print("here")
if HighestVarRed < 0.60:
check6 += 1
if HighestVarRed < 0.70:
check7 += 1
if HighestVarRed < 0.80:
check8 += 1
if HighestVarRed < 0.90:
check9 += 1
if HighestVarRed < 1:
check10 += 1
HighVarRed.remove(HighestVarRed)
#print(check1, check2, check3, check4, check5, check6, check7, check8, check9, check10)
if check >= NumParams:
RedClusters += 1
dfList = list()
for x in VarRedXDict[clusterID]:
keyName = x+str("_X")
if keyName not in clustersData[clusterID]:
clustersData[clusterID][keyName] = list()
dfList.append(keyName)
y = 0
for val in VarRedXDict[clusterID][x]:
dfList.append(val)
y += 1
if y > len(VarRedXDict[clusterID][x])-2:
clustersData[clusterID][keyName].append(val)
#dfList.append(VarRedXDict[clusterID][x])
#df3 = df3.append({clusterID: x+str("_X")}, ignore_index=True)
#df3 = df3.append({clusterID: VarRedXDict[clusterID][x]}, ignore_index=True)
for x in VarRedYDict[clusterID]:
keyName = x+str("_Y")
if keyName not in clustersData[clusterID]:
clustersData[clusterID][keyName] = list()
dfList.append(keyName)
y = 0
for val in VarRedYDict[clusterID][x]:
dfList.append(val)
y += 1
if y > len(VarRedYDict[clusterID][x])-2:
clustersData[clusterID][keyName].append(val)
#dfList.append(VarRedYDict[clusterID][x])
#df3 = df3.append({clusterID: x+str("_Y")}, ignore_index=True)
#df3 = df3.append({clusterID: VarRedYDict[clusterID][x]}, ignore_index=True)
for x in VarRedPDict[clusterID]:
keyName = x+str("_P")
if keyName not in clustersData[clusterID]:
clustersData[clusterID][keyName] = list()
dfList.append(keyName)
y = 0
for val in VarRedPDict[clusterID][x]:
dfList.append(val)
y += 1
if y > len(VarRedPDict[clusterID][x])-2:
clustersData[clusterID][keyName].append(val)
#dfList.append(VarRedPDict[clusterID][x])
#df3 = df3.append({clusterID: x+str("_P")}, ignore_index=True)
#df3 = df3.append({clusterID: VarRedPDict[clusterID][x]}, ignore_index=True)
for x in VarRedCDict[clusterID]:
keyName = x+str("_C")
if keyName not in clustersData[clusterID]:
clustersData[clusterID][keyName] = list()
dfList.append(keyName)
y = 0
for val in VarRedCDict[clusterID][x]:
dfList.append(val)
y += 1
if y > len(VarRedCDict[clusterID][x])-2:
clustersData[clusterID][keyName].append(val)
clustersData[clusterID][keyName].append(min(allDictC))
clustersData[clusterID][keyName].append(max(allDictC))
#dfList.append(VarRedCDict[clusterID][x])
#df3 = df3.append({clusterID: x}, ignore_index=True)
#df3 = df3.append({clusterID: VarRedCDict[clusterID][x]}, ignore_index=True)
#df3 = df3.dropna()
df3[clusterID] = pd.Series(dfList)
df3.to_excel(writer, sheet_name=str(layerX))
PercOfRedClusters = (RedClusters/totalClusters)*100.0
AvgVarDisp = 0
counterX = 0
print("reduced", RedClusters)
print("Perc%%%%", PercOfRedClusters)
#for KP in range(0, 27):
# Val1 = sum(AvgVarDictX[KP])/len(AvgVarDictX[KP])
# Val2 = sum(AvgVarDictY[KP])/len(AvgVarDictY[KP])
# Val3 = sum(AvgVarDictP[KP])/len(AvgVarDictP[KP])
# AvgVarDisp += Val1 + Val2 + Val3
# counterX += 3
for j in range(0, counter):
AvgVarDisp += sum(AvgVarDictC[j])/len(AvgVarDictC[j])
counterX += 1
print("Total Clusters,", "% of clusters,", "Avg var of highest params,", "Avg var of all params")
print(str(totalClusters), "%.2f" % PercOfRedClusters,
"%.2f" % float(100-((sum(HighestVarRedOverClusters)/len(HighestVarRedOverClusters))*100.0)),
"%.2f" % float(100-((AvgVarDisp/counterX)*100.0)))
totalClustersList.append(totalClusters)
PercOfRedClustersList.append(PercOfRedClusters)
HighestVarRedOverClustersList.append(float(100-((sum(HighestVarRedOverClusters)/len(HighestVarRedOverClusters))*100.0)))
AvgVarDispList.append(float(100-((AvgVarDisp/counterX)*100.0)))
#print("Average Variance reduction of all params over all clusters:", 100-((AvgVarDisp/counter)*100.0))
#print("Percentage of clusters with at least 3 param having >50% reduction:", PercOfRedClusters, "%")
#print("Average variance of highest param over all clusters:", 100-(((sum(HighestVarRedOverClusters)/len(HighestVarRedOverClusters))*100.0)))
#print("Total Non-single Clusters:", totalClusters)
#print("Median-AD Reduction:", AvgMadDisp/counter)
#print("Mean-AD Reduction:", AvgMedDisp/counter)
labelList = list()
AvgICDList = list()
SilList = list()
newAvgICDList = list()
for layer in layers:
layerClust = join(outputPath, caseFile["RCC"], "ClusterAnalysis_" + str(clustMode), layer + ".pt")
clsData = torch.load(layerClust,map_location=torch.device('cpu'))
labelListed = clsData['label list']
labelList.append([(i) for i in labelListed])
AvgICDList.append(clsData['avgLayer'])
newAvgICDList.append(clsData['avgLayer'])
SilList.append(clsData['silhouette'])
wb = Workbook()
#wb.save(clustexcel)
red = [0] * (11)
discarded = [0] * (11)
df4 = pd.DataFrame()
AvgICDList[0] = 50000000000
SilList[0] = -50000000000
ChosenLayers = [''] * 3
ChosenLayers[0] = bestlayer(red, min(red), layers)
ChosenLayers[1] = bestlayer(AvgICDList, min(AvgICDList), layers)
ChosenLayers[2] = bestlayer(SilList, max(SilList), layers)
df4["layers"] = layers
df4["total Clusters"] = totalClustersList
df4["percentage of reduced Clusters"] = PercOfRedClustersList
df4["Avg var reduction of highest 3 params"] = HighestVarRedOverClustersList
df4["Avg var reduction of all params"] = AvgVarDispList
df4["Silhouette"] = SilList
df4["avgLayerICD"] = AvgICDList
df4["Silh"] = pd.Series(bestlayer(SilList, max(SilList), layers))
df4["AvgICD"] = pd.Series(bestlayer(AvgICDList, min(AvgICDList), layers))
#df4["AvgRedOf1"] = pd.Series(bestlayer(HighestVarRedOverClustersList, max(HighestVarRedOverClustersList), layers))
df4["AvgRedOf3"] = pd.Series(bestlayer(HighestVarRedOverClustersList, max(HighestVarRedOverClustersList), layers))
#df4["AvgRedOf3below50"] = pd.Series(bestlayer(HighestVarRedOverClustersList, max(HighestVarRedOverClustersList), layers))
#df4["AvgRedOf4"] = pd.Series(bestlayer(HighestVarRedOverClustersList, max(HighestVarRedOverClustersList), layers))
df4["AvgRedOfAll"] = pd.Series(bestlayer(AvgVarDispList, max(AvgVarDispList), layers))
#df4["AvgRedOfAllbelow50"] = pd.Series(bestlayer(AvgVarDispList, max(AvgVarDispList), layers))
df4.to_excel(writer, sheet_name="Summary")
#sheet4 = wb.create_sheet("Summary", 0)
#i = 0
#for x in dataframe_to_rows(df4):
# if (i != 1):
# sheet4.append(x)
# i = i + 1
#wb.save(clustexcel)
writer.close()
return clustersData
def thresholds(v, th):
if v < th:
print(th)
return True