def build_from_data(self, twn, docs, max_depth=5, min_doc_num=20):
starttime = time.time()
cowordnet = self.build_global_cowordnet(docs)
com_dect = comdect.LabelCommunityDetection(min_nodes=30)
group = CommunityGroup(cowordnet, docs, com_dect)
real_labels = None
if self.logfile:
real_labels = self.load_doc_labels(docs)
self.resoutput = open(self.logfile, 'w')
self.resoutput.write(
'NCluster \tAdjusted_Rand \tAdjusted_NMI \tF-Measure \tV-Measure \tprecison \tcall\n'
)
depth = 0
rootcom = Community(twn, group, len(docs))
group.add_community(rootcom)
while depth <= max_depth:
acoms = group.active_coms()
if not acoms: break
print 'dividing community'
for c in acoms:
children = c.make_children()
if children:
group.remove_community(c)
for ch in children:
group.add_community(ch)
acoms = group.active_coms()
if not acoms: break
uncdocs = group.unclassified_docs()
print 'Mapping unclassified document into communities'
Community.map_docs_to_coms(uncdocs, acoms)
group.remove_null_community(min_doc_num)
depth += 1
if self.logfile:
predicted = group.doc_labels()
rs = cmp_cluster_result(predicted, len(group), real_labels)
self.resoutput.write(rs)
print 'rebuilding wordnet'
for c in acoms:
c.rebuild_wordnet()
self.merge_communities(group, 0.5)
if self.logfile:
predicted = group.doc_labels()
rs = cmp_cluster_result(predicted, len(group), real_labels)
self.resoutput.write(rs)
self.resoutput.write('\r\n')
self.resoutput.write(self.output_keywords(group).encode('utf8'))
self.resoutput.close()
#os.system('emacs ../doc_clustering_evalution.txt')
self._run_time = time.time() - starttime
print 'Elapsed time: %.2fs' % self._run_time
return group.doc_labels()
def build_from_data(self,twn,docs,max_depth=5, min_doc_num=20):
starttime = time.time()
cowordnet = self.build_global_cowordnet(docs)
com_dect = comdect.LabelCommunityDetection(min_nodes=30)
group = CommunityGroup(cowordnet,docs,com_dect)
real_labels = None
if self.logfile:
real_labels = self.load_doc_labels(docs)
self.resoutput = open(self.logfile,'w')
self.resoutput.write('NCluster \tAdjusted_Rand \tAdjusted_NMI \tF-Measure \tV-Measure \tprecison \tcall\n')
depth = 0
rootcom = Community(twn,group,len(docs))
group.add_community(rootcom)
while depth<=max_depth:
acoms = group.active_coms()
if not acoms: break
print 'dividing community'
for c in acoms:
children = c.make_children()
if children:
group.remove_community(c)
for ch in children:
group.add_community(ch)
acoms = group.active_coms()
if not acoms: break
uncdocs = group.unclassified_docs()
print 'Mapping unclassified document into communities'
Community.map_docs_to_coms(uncdocs, acoms)
group.remove_null_community(min_doc_num)
depth += 1
if self.logfile:
predicted = group.doc_labels()
rs = cmp_cluster_result(predicted,len(group),real_labels)
self.resoutput.write(rs)
print 'rebuilding wordnet'
for c in acoms:
c.rebuild_wordnet()
self.merge_communities(group, 0.5)
if self.logfile:
predicted = group.doc_labels()
rs = cmp_cluster_result(predicted,len(group),real_labels)
self.resoutput.write(rs)
self.resoutput.write('\r\n')
self.resoutput.write(self.output_keywords(group).encode('utf8'))
self.resoutput.close()
#os.system('emacs ../doc_clustering_evalution.txt')
self._run_time = time.time() - starttime
print 'Elapsed time: %.2fs' % self._run_time
return group.doc_labels()
def load_title_wordnet(self, min_coocur=2, min_weight=1e-3):
titleiter = Community.wordpair_weight(self.iter_title_words(),
min_coocur, min_weight)
elist = []
for w1, w2, co, weight in titleiter:
elist.append({'source': w1, 'target': w2, 'weight': weight})
return igraph.Graph.DictList(vertices=None, edges=elist)
def merge_communities(self, group, merge_freshold=0.5):
n = len(group)
dset = DisjoinSet(n)
coms = list(iter(group))
for i in range(0,n-1):
for j in range(i+1,n):
sim = Community.similarity(coms[i],coms[j])
#print 'similarity: %.5f' % sim
if sim > merge_freshold:
dset.union(i,j)
clusters = dset.sets(min_size=2)
for c in clusters:
group.merge_communities([coms[i] for i in c])
def merge_communities(self, group, merge_freshold=0.5):
n = len(group)
dset = DisjoinSet(n)
coms = list(iter(group))
for i in range(0, n - 1):
for j in range(i + 1, n):
sim = Community.similarity(coms[i], coms[j])
#print 'similarity: %.5f' % sim
if sim > merge_freshold:
dset.union(i, j)
clusters = dset.sets(min_size=2)
for c in clusters:
group.merge_communities([coms[i] for i in c])
def build_global_cowordnet(self, docs, min_coocur=2):
dociter = Community.wordpair_weight(docs, min_coocur,0)
# import itertools
# co_dict = dict()
# for words in docs:
# for wp in itertools.combinations(words,2):
# if wp[1]>wp[0]: wp = (wp[1],wp[0])
# try:
# co_dict[wp] += 1
# except:
# co_dict[wp] = 1
def dict2list(docs):
for w1,w2,co,weight in docs:
yield {'source':w1, 'target':w2, 'weight':co}
return igraph.Graph.DictList(vertices=None, edges=dict2list(dociter))
def build_global_cowordnet(self, docs, min_coocur=2):
dociter = Community.wordpair_weight(docs, min_coocur, 0)
# import itertools
# co_dict = dict()
# for words in docs:
# for wp in itertools.combinations(words,2):
# if wp[1]>wp[0]: wp = (wp[1],wp[0])
# try:
# co_dict[wp] += 1
# except:
# co_dict[wp] = 1
def dict2list(docs):
for w1, w2, co, weight in docs:
yield {'source': w1, 'target': w2, 'weight': co}
return igraph.Graph.DictList(vertices=None, edges=dict2list(dociter))
def load_title_wordnet(self,min_coocur=2, min_weight=1e-3):
titleiter = Community.wordpair_weight(self.iter_title_words(), min_coocur, min_weight)
elist = []
for w1,w2,co,weight in titleiter:
elist.append({'source':w1, 'target':w2, 'weight':weight})
return igraph.Graph.DictList(vertices=None, edges=elist)
