def KM(domain, n_clusters):
if domain == 'DietType':
X = getDietTypeTFArray4DC()
elif domain == 'ActType':
X = getActTypeTFArray4DC()
X = utilise.normArray(X)
reduced_data = PCA(n_components=2).fit_transform(X)
Inertia = []
Labels = []
# for n_clusters in range_n_clusters:
for j in range(300):
kmeans = KMeans(init='k-means++', n_clusters=n_clusters, n_init=10)
kmeans.fit(reduced_data)
inertia = kmeans.inertia_
Inertia.append(inertia)
labels = kmeans.labels_
Labels.append(labels)
min = np.min(Inertia)
for i in range(len(Inertia)):
if Inertia[i] == min:
inertia = Inertia[i]
labels = Labels[i]
print domain,n_clusters,inertia, labels
def KM_nonslp(domain, n_clusters):
if domain == 'DietType':
X = dataGen4DietAct.genDietTypeTFArrayWithSlp()
else:
X = dataGen4DietAct.genActTypeTFArrayWithSlp()
X = utilise.normArray(X)
Inertia = []
Labels = []
for j in range(300):
kmeans = KMeans(init='k-means++', n_clusters=n_clusters, n_init=10)
kmeans.fit(X)
inertia = kmeans.inertia_
Inertia.append(inertia)
labels = kmeans.labels_
Labels.append(labels)
min = np.min(Inertia)
for i in range(len(Inertia)):
if Inertia[i] == min:
inertia = Inertia[i]
labels = Labels[i]
print domain, n_clusters, inertia, labels
def sihouetteScoreArtificialData(metric):
df, cols = artificialDataGenerator.artificialData()
for domain in Domain:
print df.columns
if domain == 'DietType':
df_temp = df[[
'alcoholD', 'caffeineD', 'compositeP', 'dairyP', 'eggP',
'fruitP', 'grainP', 'meatP', 'seafood', 'snack', 'starchyP',
'vegetables'
]]
else:
df_temp = df[[
'entertainmentRelax', 'others', 'social', 'sport',
'transportation1', 'transportation2', 'transportation3',
'workStudy'
]]
X = df_temp.as_matrix()
X = utilise.normArray(X)
range_n_clusters = [2, 3, 4, 5, 6]
for n_clusters in range_n_clusters:
clusterer = KMeans(n_clusters=n_clusters, n_init=300)
clusterer.fit(X)
cluster_labels = clusterer.labels_
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print(metric, domain, 'For n_clusters =', n_clusters,
'The average silhouette_score is :', silhouette_avg)
def sihouetteScore(metric):
for domain in Domain:
if metric == 'TF':
if domain == 'DietType':
X = dataGen4DietAct.genDietTypeTFArray()
elif domain == 'ActType':
X = dataGen4DietAct.genActTypeTFArray()
elif metric == 'TFIDF':
if domain == 'DietType':
X = dataGen4DietAct.DietTypeTfidfArray()
elif domain == 'ActType':
X = dataGen4DietAct.ActTypeTfidfArray()
X = utilise.normArray(X)
reduced_data = PCA(n_components=2).fit_transform(X)
range_n_clusters = [2, 3, 4, 5, 6]
for n_clusters in range_n_clusters:
clusterer = KMeans(n_clusters=n_clusters, n_init=300)
clusterer.fit(reduced_data)
cluster_labels = clusterer.labels_
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print(metric, domain, 'For n_clusters =', n_clusters,
'The average silhouette_score is :', silhouette_avg)
def visTFIDFMatrix():
tfidf1 = utilise.normArray(dataGen4DietAct.ActItemTfidfArray())
# tfidf1 = dataGen4DietAct.ActItemTfidfArray()
plt.figure()
plt.matshow(tfidf1)
plt.colorbar()
plt.title('actTFIDFMatrix')
plt.savefig('visTForTFIDFMatrix/actTFIDFMatrix')
tfidf2 = utilise.normArray(dataGen4DietAct.DietItemTfidfArray())
# tfidf2 = dataGen4DietAct.DietItemTfidfArray()
plt.figure()
plt.matshow(tfidf2)
plt.colorbar()
plt.title('dietTFIDFMatrix')
plt.savefig('visTForTFIDFMatrix/dietTFIDFMatrix')
tfidf = utilise.genCombiArray(tfidf1, tfidf2)
plt.figure()
plt.matshow(tfidf)
plt.colorbar()
plt.title('actDietTFIDFMatrix')
plt.savefig('visTForTFIDFMatrix/actDietTFIDFMatrix')
tfidf2 = utilise.normArray(dataGen4DietAct.DietTypeTfidfArray())
# tfidf2 = dataGen4DietAct.DietTypeTfidfArray()
plt.figure()
plt.matshow(tfidf2)
plt.colorbar()
plt.title('dietTypeTFIDFMatrix')
plt.savefig('visTForTFIDFMatrix/dietTypeTFIDFMatrix')
tfidf1 = utilise.normArray(dataGen4DietAct.ActTypeTfidfArray())
# tfidf1 = dataGen4DietAct.ActTypeTfidfArray()
plt.figure()
plt.matshow(tfidf1)
plt.colorbar()
plt.title('actTypeTFIDFMatrix')
plt.savefig('visTForTFIDFMatrix/actTypeTFIDFMatrix')
tfidf = utilise.genCombiArray(tfidf1, tfidf2)
plt.figure()
plt.matshow(tfidf)
plt.colorbar()
plt.title('actDietTypeTFIDFMatrix')
plt.savefig('visTForTFIDFMatrix/actDietTypeTFIDFMatrix')
def visTFMatrix():
tf_ActItem = utilise.normArray(dataGen4DietAct.genActItemTFArray())
# tf_ActItem = dataGen4DietAct.genActItemTFArray()
plt.figure()
plt.matshow(tf_ActItem)
plt.colorbar()
plt.title('actTFMatrix')
plt.savefig('visTForTFIDFMatrix/actTFMatrix')
tf_DietItem = utilise.normArray(dataGen4DietAct.genDietItemTFArray())
# tf_DietItem = dataGen4DietAct.genDietItemTFArray()
plt.figure()
plt.matshow(tf_DietItem)
plt.colorbar()
plt.title('dietTFMatrix')
plt.savefig('visTForTFIDFMatrix/dietTFMatrix')
tf = utilise.genCombiArray(tf_ActItem,tf_DietItem)
plt.figure()
plt.matshow(tf)
plt.colorbar()
plt.title('actDietTFMatrix')
plt.savefig('visTForTFIDFMatrix/actDietTFMatrix')
tf_DietType = utilise.normArray(dataGen4DietAct.genDietTypeTFArray())
# tf_DietType = dataGen4DietAct.genDietTypeTFArray()
plt.figure()
plt.matshow(tf_DietType)
plt.colorbar()
plt.title('dietTypeTFMatrix')
plt.savefig('visTForTFIDFMatrix/dietTypeTFMatrix')
tf_ActType = utilise.normArray(dataGen4DietAct.genActTypeTFArray())
# tf_ActType = dataGen4DietAct.genActTypeTFArray()
plt.figure()
plt.matshow(tf_ActType)
plt.colorbar()
plt.title('actTypeTFMatrix')
plt.savefig('visTForTFIDFMatrix/actTypeTFMatrix')
tf = utilise.genCombiArray(tf_ActType,tf_DietType)
plt.figure()
plt.matshow(tf)
plt.colorbar()
plt.title('actDietTypeTFMatrix')
plt.savefig('visTForTFIDFMatrix/actDietTypeTFMatrix')
def KM_AtificialData():
df, cols = artificialDataGenerator.artificialData()
for domain in Domain:
print df.columns
if domain == 'DietType':
df_temp = df[[
'alcoholD', 'caffeineD', 'compositeP', 'dairyP', 'eggP',
'fruitP', 'grainP', 'meatP', 'seafood', 'snack', 'starchyP',
'vegetables'
]]
else:
df_temp = df[[
'entertainmentRelax', 'others', 'social', 'sport',
'transportation1', 'transportation2', 'transportation3',
'workStudy'
]]
X = df_temp.as_matrix()
X = utilise.normArray(X)
range_n_clusters = [2, 3, 4, 5, 6]
for n_clusters in range_n_clusters:
kmeans = KMeans(n_clusters=n_clusters, n_init=3000)
kmeans.fit(X)
labels = kmeans.labels_
inertia = kmeans.inertia_
plt.figure()
reduced_data = PCA(n_components=2).fit_transform(X)
N = np.max(labels) + 1
for k in range(N):
class_members = labels == k
if k == 0:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'go', markersize=5)
if k == 1:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'ro', markersize=5)
if k == 2:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'bo', markersize=5)
if k == 3:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'yo', markersize=5)
# for i in range(reduced_data.shape[0]):
# plt.text(reduced_data[i, 0], reduced_data[i, 1],i)
plt.title('K-means clustering (PCA-reduced data)')
plt.savefig('visClustering' + domain +
'Pattern/KMeans_TF_artificial_' + str(n_clusters))
print domain, n_clusters, inertia, labels
def sihouetteScore4DC(metric):
for domain in Domain:
if domain == 'DietType':
X = validation4DC.getDietTypeTFArray4DC()
elif domain == 'ActType':
X = validation4DC.getActTypeTFArray4DC()
X = utilise.normArray(X)
range_n_clusters = [2, 3, 4, 5, 6]
for n_clusters in range_n_clusters:
clusterer = KMeans(n_clusters=n_clusters, n_init=300)
clusterer.fit(X)
cluster_labels = clusterer.labels_
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print(metric, domain, 'For n_clusters =', n_clusters,
'The average silhouette_score is :', silhouette_avg)
def singleSubjectDailyArray(domain, subjectID):
'''
build daily item TFIDF normalization array
'''
if domain == 'ActItem':
item_dict = dataGen4DietAct.genActItemDict()
elif domain == 'DietItem':
item_dict = dataGen4DietAct.genDietItemDict()
duration = dietActInfoRetrv.getDuration(subjectID)
x = duration
n = len(item_dict)
dims = (x, n)
array = np.zeros(dims)
if domain == 'ActItem':
for i in range(duration):
ItemIndex = buildItemIndex.build_daily_single_activity_index(
subjectID, i + 1)
for key in item_dict:
if "'" + item_dict[key] + "'" in ItemIndex:
array[i, key] = ItemIndex["'" + item_dict[key] + "'"]
if domain == 'DietItem':
for i in range(duration):
ItemIndex = buildItemIndex.build_daily_single_diet_index(
subjectID, i + 1)
for key in item_dict:
if "'" + item_dict[key] + "'" in ItemIndex:
array[i, key] = ItemIndex["'" + item_dict[key] + "'"]
transformer = TfidfTransformer(norm=None)
tfidf = transformer.fit_transform(array)
aa = tfidf.toarray()
tfidfNorm = utilise.normArray(aa)
print tfidfNorm.shape
return tfidfNorm
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 16 20:12:45 2016
@author: jingjing
"""
from sklearn.cluster import DBSCAN
import utilise
Domain = ['DietType', 'ActType']
for domain in Domain:
if domain == 'DietType':
X = utilise.genDietTypeTFArray()
elif domain == 'ActType':
X = utilise.genActTypeTFArray()
X = utilise.normArray(X)
db = DBSCAN(0.8, 1).fit(X)
labels = db.labels_
print db.components_
print labels
def buildSubAveInfo():
workbookW = xlwt.Workbook()
ws = workbookW.add_sheet('AveInfo')
groupAct = dietActInfoRetrv.getGroups(labelsActType)
groupDiet = dietActInfoRetrv.getGroups(labelsDietType)
Age, Gender, Height, Weight, BMI, FatFree, FatMass, PercFat, Vo2max = slpInfoRetrv.getDemoGInfo(
)
SlpHours = slpInfoRetrv.getSlpHours()
MedianHR = slpInfoRetrv.getMedianHR()
MedianHRBefore = slpInfoRetrv.getMedianHRBefore()
MedianHRAfter = slpInfoRetrv.getMedianHRAfter()
titles = [
'SubjId', 'ActGroup', 'DietGroup', 'HoursSleep', 'MedianHR',
'MedianHRBefore', 'MedianHRAfter', 'age', 'gender', 'height', 'weight',
'BMI', 'FatFreeMass', 'FatMass', 'PercFat', 'vo2max'
]
for i in range(len(titles)):
ws.write(0, i, titles[i])
rowW = 1
for index in range(len(sleep_list)):
ws.write(rowW, 0, sleep_list[index])
for key in groupAct:
if sleep_list[index] in groupAct[key]:
ws.write(rowW, 1, key)
break
for key in groupDiet:
if sleep_list[index] in groupDiet[key]:
ws.write(rowW, 2, key)
break
ws.write(rowW, 1 + 2, SlpHours[index])
ws.write(rowW, 2 + 2, MedianHR[index])
ws.write(rowW, 3 + 2, MedianHRBefore[index])
ws.write(rowW, 4 + 2, MedianHRAfter[index])
ws.write(rowW, 5 + 2, Age[index])
ws.write(rowW, 6 + 2, Gender[index])
ws.write(rowW, 7 + 2, Height[index])
ws.write(rowW, 8 + 2, Weight[index])
ws.write(rowW, 9 + 2, BMI[index])
ws.write(rowW, 10 + 2, FatFree[index])
ws.write(rowW, 11 + 2, FatMass[index])
ws.write(rowW, 12 + 2, PercFat[index])
ws.write(rowW, 13 + 2, Vo2max[index])
rowW += 1
ws2 = workbookW.add_sheet('DietTF')
row_labels = utilise.itemDict2list(dataGen4DietAct.genDietTypeDict())
X = utilise.normArray(dataGen4DietAct.genDietTypeTFArray())
ws2.write(0, 0, 'SubjId')
ws2.write(0, 1, 'DietGroup')
for i in range(len(row_labels)):
ws2.write(0, i + 2, row_labels[i])
rowW = 1
for index in range(len(available_list)):
ws2.write(rowW, 0, available_list[index])
for key in groupDiet:
if available_list[index] in groupDiet[key]:
ws2.write(rowW, 1, key)
break
for i in range(len(row_labels)):
ws2.write(rowW, i + 2, X[index][i])
rowW += 1
ws3 = workbookW.add_sheet('ActTF')
row_labels = utilise.itemDict2list(dataGen4DietAct.genActTypeDict())
X = utilise.normArray(dataGen4DietAct.genActTypeTFArray())
ws3.write(0, 0, 'SubjId')
ws3.write(0, 1, 'ActGroup')
for i in range(len(row_labels)):
ws3.write(0, i + 2, row_labels[i])
rowW = 1
for index in range(len(available_list)):
ws3.write(rowW, 0, available_list[index])
for key in groupAct:
if available_list[index] in groupAct[key]:
ws3.write(rowW, 1, key)
break
for i in range(len(row_labels)):
ws3.write(rowW, i + 2, X[index][i])
rowW += 1
workbookW.save('SubAveInfo.xls')
def KM(domain, n_clusters):
# if domain == 'DietType':
# X = dataGen4DietAct.genDietTypeTFArray()
# elif domain == 'ActType':
# X = dataGen4DietAct.genActTypeTFArray()
# X = utilise.normArray(X)
# if domain == 'DietType':
# Similarity_dict = utilise.SimilarityDict(domain,'TFEclud')
# elif domain == 'ActType':
# Similarity_dict = utilise.SimilarityDict(domain,'TFEclud')
# X = visSimilarityMat.similarityDict2array(Similarity_dict,0)
if domain == 'DietType':
X = validation4DC.getDietTypeTFArray4DC()
elif domain == 'ActType':
X = validation4DC.getActTypeTFArray4DC()
X = utilise.normArray(X)
# print X
# print X.shape
Inertia = []
Labels = []
# range_n_clusters = [2, 3, 4, 5, 6]
# range_n_clusters = [4]
# for n_clusters in range_n_clusters:
for j in range(300):
kmeans = KMeans(init='k-means++', n_clusters=n_clusters, n_init=10)
# kmeans.fit(reduced_data)
kmeans.fit(X)
inertia = kmeans.inertia_
Inertia.append(inertia)
# print domain,inertia
labels = kmeans.labels_
Labels.append(labels)
# print labels
min = np.min(Inertia)
for i in range(len(Inertia)):
if Inertia[i] == min:
inertia = Inertia[i]
labels = Labels[i]
plt.figure()
reduced_data = PCA(n_components=2).fit_transform(X)
N = np.max(labels) + 1
for k in range(N):
class_members = labels == k
if k == 0:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'go', markersize=5)
if k == 1:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'ro', markersize=5)
if k == 2:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'bo', markersize=5)
if k == 3:
for x in reduced_data[class_members]:
plt.plot(x[0], x[1], 'yo', markersize=5)
for i in range(reduced_data.shape[0]):
plt.text(reduced_data[i, 0], reduced_data[i, 1], i)
plt.title('K-means clustering (PCA-reduced data)')
plt.savefig('visClustering' + domain + 'Pattern/KMeans_TF_' +
str(n_clusters))
# a,b = kMeans(X,2)
# print b[:,0].shape
# print a,b[:,0].ravel()
# print sum(b[:,1].ravel())
print domain, n_clusters, inertia, labels
def clusteringKmeansLabelsNewSubs():
workbookW = xlwt.Workbook()
ws = workbookW.add_sheet('sheet1')
rowW = 0
df = newDataProcess.newSubInfo()
for domain in Domain:
print df.columns
if domain == 'DietType':
df_temp = df[['alcoholD','caffeineD','dairyP','eggP','fruitP','grainP','meatP','seafood','snack','starchyP','vegetables']]
row_labels = df_temp.columns
X = df_temp.as_matrix()
X = utilise.normArray(X)
kmeans = KMeans(n_clusters=2, n_init = 3000)
kmeans.fit(X)
labels = kmeans.labels_
else:
df_temp = df[['leisure','social','sport','walk','car','bike','workStudy']]
row_labels = df_temp.columns
X = df_temp.as_matrix()
X = utilise.normArray(X)
kmeans = KMeans(n_clusters=3, n_init = 3000)
kmeans.fit(X)
labels = kmeans.labels_
# write the lables to excel file
col = 0
for label in row_labels:
ws.write(rowW,col,label)
col += 1
rowW += 1
# print type(labels)
plt.figure()
n_clusters = np.max(labels) + 1
for k in range(n_clusters):
class_members = labels == k
group = []
for x in X[class_members]:
group.append(x)
group = np.array(group)
meanVec = np.mean(group,axis=0)
meanVec.tolist()
stdVec = np.std(group,axis=0)
stdVec.tolist()
print stdVec
# write the mean vector of each group to excel file
col = 0
for value in meanVec:
ws.write(rowW,col,value)
col += 1
rowW += 1
# print meanVec
firstMax = np.max(meanVec)
# print firstMax
tempVec = np.copy(meanVec)
for j in range(X.shape[1]):
if tempVec[j] == firstMax:
tempVec[j] = 0
secondMax = np.max(tempVec)
# print secondMax
tempVec2 = np.copy(tempVec)
for j in range(X.shape[1]):
if tempVec2[j]==secondMax:
tempVec2[j] = 0
thirdMax = np.max(tempVec2)
# print thirdMaxO
x = range(X.shape[1])
plt.plot(x,meanVec)
# plt.errorbar(x,meanVec,yerr=stdVec)
# print meanVec
for j in range(X.shape[1]):
# if meanVec[j] == firstMax:
# if meanVec[j] == firstMax or meanVec[j] == secondMax:
if meanVec[j] == firstMax or meanVec[j] == secondMax or meanVec[j] == thirdMax:
ws.write(rowW,0,k)
ws.write(rowW,1,domain)
ws.write(rowW,2,row_labels[j])
ws.write(rowW,3,meanVec[j])
rowW += 1
print k,domain,n_clusters,meanVec[j],row_labels[j]
plt.text(x[j],meanVec[j],row_labels[j])
# print row_labels
# plt.xlabel(row_labels)
plt.title(domain+'_TF_KMeans_'+str(n_clusters))
plt.savefig('visClustering'+domain+'Pattern/KMeans__TF_NewDataSubs_'+str(n_clusters)+'_groupFreq')
workbookW.save('tempLabels.xls')
def bestLabel(labelsDietType,labelsActType):
workbookW = xlwt.Workbook()
ws = workbookW.add_sheet('sheet1')
rowW = 0
for domain in Domain:
if domain == 'DietType':
labels = utilise.string2array(labelsDietType)
row_labels = utilise.itemDict2list(dataGen4DietAct.genDietTypeDict())
X = dataGen4DietAct.genDietTypeTFArray()
elif domain == 'ActType':
labels = utilise.string2array(labelsActType)
row_labels = utilise.itemDict2list(dataGen4DietAct.genActTypeDict())
X = dataGen4DietAct.genActTypeTFArray()
X = utilise.normArray(X)
# write the lables to excel file
col = 0
for label in row_labels:
ws.write(rowW,col,label)
col += 1
rowW += 1
# print type(labels)
plt.figure()
n_clusters = np.max(labels) + 1
for k in range(n_clusters):
class_members = labels == k
group = []
for x in X[class_members]:
group.append(x)
group = np.array(group)
meanVec = np.mean(group,axis=0)
meanVec.tolist()
stdVec = np.std(group,axis=0)
stdVec.tolist()
# write the mean vector of each group to excel file
col = 0
for value in meanVec:
ws.write(rowW,col,value)
col += 1
rowW += 1
# print meanVec
# we don't have to do normalization here, as the input X has already been normalized
# totalSum = np.sum(meanVec[0])
# print totalSum
# meanVec = meanVec/totalSum
# # normalize the meanVec
# firstMax = np.max(meanVec)
# meanVec = meanVec/firstMax
firstMax = np.max(meanVec)
# print firstMax
tempVec = np.copy(meanVec)
for j in range(X.shape[1]):
if tempVec[j] == firstMax:
tempVec[j] = 0
secondMax = np.max(tempVec)
# print secondMax
tempVec2 = np.copy(tempVec)
for j in range(X.shape[1]):
if tempVec2[j]==secondMax:
tempVec2[j] = 0
thirdMax = np.max(tempVec2)
# print thirdMax
x = range(X.shape[1])
plt.plot(x,meanVec)
# print meanVec
for j in range(X.shape[1]):
# if meanVec[j] == firstMax:
# if meanVec[j] == firstMax or meanVec[j] == secondMax:
if meanVec[j] == firstMax or meanVec[j] == secondMax or meanVec[j] == thirdMax:
print k,domain,n_clusters,meanVec[j],row_labels[j]
plt.text(x[j],meanVec[j],row_labels[j])
# print row_labels
# plt.xlabel(row_labels)
plt.title(domain+'_TF_KMeans_'+str(n_clusters))
plt.savefig('visClustering'+domain+'Pattern/KMeans__TF_'+str(n_clusters)+'_groupFreq')
workbookW.save('tempLabels.xls')
def HC(domain, para):
if para in Metric:
if para == 'TF':
if domain == 'DietItem':
X = dataGen4DietAct.genDietItemTFArray()
elif domain == 'ActItem':
X = dataGen4DietAct.genActItemTFArray()
elif domain == 'DietType':
X = dataGen4DietAct.genDietTypeTFArray()
elif domain == 'ActType':
X = dataGen4DietAct.genActTypeTFArray()
elif para == 'TFIDF':
if domain == 'DietItem':
X = dataGen4DietAct.DietItemTfidfArray()
elif domain == 'ActItem':
X = dataGen4DietAct.ActItemTfidfArray()
elif domain == 'DietType':
X = dataGen4DietAct.DietTypeTfidfArray()
elif domain == 'ActType':
X = dataGen4DietAct.ActTypeTfidfArray()
X = utilise.normArray(X)
if para in Sim:
Similarity_dict = {}
if domain == 'DietItem':
Similarity_dict = utilise.SimilarityDict(domain, para)
elif domain == 'ActItem':
Similarity_dict = utilise.SimilarityDict(domain, para)
elif domain == 'DietType':
Similarity_dict = utilise.SimilarityDict(domain, para)
elif domain == 'ActType':
Similarity_dict = utilise.SimilarityDict(domain, para)
X = visSimilarityMat.similarityDict2array(Similarity_dict, 0)
# method can be ward, complete, average
method = 'ward'
row_method = method
row_metric = 'euclidean'
column_method = method
column_metric = 'euclidean'
# http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.spatial.distance.pdist.html
# d1 = ssd.pdist(X,'cosine')
d1 = ssd.pdist(X)
# http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.spatial.distance.squareform.html#scipy.spatial.distance.squareform
D1 = ssd.squareform(d1) # full matrix
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage
Y1 = sch.linkage(D1, method=row_method, metric=row_metric)
row_idxing = sch.leaves_list(Y1)
# http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.spatial.distance.pdist.html
d2 = ssd.pdist(X.T)
# http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.spatial.distance.squareform.html#scipy.spatial.distance.squareform
D2 = ssd.squareform(d2)
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage
Y2 = sch.linkage(D2, method=column_method, metric=column_metric)
col_idxing = sch.leaves_list(Y2)
heatmap_array = X[:, col_idxing][
row_idxing, :] #a numpy.ndarray or numpy.matrix, for this example, let's say mxn array
top_dendrogram = Y2 #a (n-1) x 4 array
side_dendrogram = Y1 #a (m-1) x 4 array
row_labels = range(X.shape[0])
if para in Sim:
col_labels = range(X.shape[1])
if para in Metric:
if domain == 'DietItem':
col_labels = utilise.itemDict2list(
dataGen4DietAct.genDietItemDict())
elif domain == 'ActItem':
col_labels = utilise.itemDict2list(
dataGen4DietAct.genActItemDict())
elif domain == 'DietType':
col_labels = utilise.itemDict2list(
dataGen4DietAct.genDietTypeDict())
elif domain == 'ActType':
col_labels = utilise.itemDict2list(
dataGen4DietAct.genActTypeDict())
col_idxing = list(col_idxing)
row_idxing = list(row_idxing)
print col_idxing
new_row_labels = []
new_col_labels = []
for i in range(len(row_idxing)):
new_row_labels.append(str(row_labels[row_idxing[i]]))
for j in range(len(col_idxing)):
new_col_labels.append(str(col_labels[col_idxing[j]]))
heatmap = pdh.DendroHeatMap(heat_map_data=heatmap_array,
left_dendrogram=side_dendrogram,
top_dendrogram=top_dendrogram)
heatmap.title = 'HC_' + domain + '_' + para + '_' + method
heatmap.row_labels = new_row_labels
heatmap.col_labels = new_col_labels
# heatmap.show()
heatmap.export('VisClustering' + domain + 'Pattern/Hierarchy_' + para +
'_' + method + '.png')
