def author_clustering_result(request):
es = Elasticsearch()
if request.GET.get("index_id") is not None and request.GET.get("index_id") != "":
index_id = request.GET.get("index_id")
authors = es.search(index_id, "author", body={"size": 10000, "query": {"match_all": {}}})["hits"]["hits"]
authors = {int(author.get("_id")): author.get("_source") for author in authors}
mapper = Mapper()
for author in authors:
mapper.create_sid(author)
N = mapper.size()
distance = numpy.zeros(shape=(N, N))
nodes = []
edges = []
for author_sid_1 in range(N):
author_uid_1 = mapper.get_uid(author_sid_1)
nodes.append(author_uid_1)
for author_sid_2 in range(N):
author_uid_2 = mapper.get_uid(author_sid_2)
co_authored_publications = set(authors[author_uid_1].get("publications")).intersection(
set(authors[author_uid_2].get("publications"))
)
if author_sid_1 < author_sid_2:
for i in range(len(co_authored_publications)):
edges.append({"from": author_uid_1, "to": author_uid_2})
distance[author_sid_1][author_sid_2] = 1.0 / (1 + 5 * len(co_authored_publications))
avc = AverageLinkClustering(distance)
clusters, cluster_map = avc.cluster(N ** 0.6 * 2)
uid_clusters = []
uid_cluster_map = {}
for cluster in clusters:
uid_clusters.append(set([mapper.get_uid(x) for x in cluster]))
for sid in cluster_map:
uid_cluster_map[mapper.get_uid(sid)] = cluster_map[sid]
# print(uid_clusters, uid_cluster_map)
# print(nodes, edges)
return render(
request,
"author_clustering_result.html",
{"graph": {"nodes": nodes, "edges": edges}, "cluster_map": uid_cluster_map, "clusters": uid_clusters},
)
indexes = es.indices.get_mapping()
return render(request, "author_clustering_result.html", {"indexes": indexes})
def calculate_pagerank_and_insert_to_elasticsearch(index_id, alpha, job_info_id):
es = Elasticsearch()
job_info = JobInfo.objects.get(id=job_info_id)
job_info.save()
job_info.info = json.dumps({'message': 'Fetching Publications From Elastic Joon ...', 'percentage': 7})
job_info.save()
publications = es.search(index_id, 'publication', body={"size": 10000, "query": {"match_all": {}}})['hits']['hits']
publications = {int(publication.get('_id')): publication.get('_source') for publication in publications}
mapper = Mapper()
for publication in publications:
mapper.create_sid(publication)
N = mapper.size()
links_graph = numpy.zeros(shape=(N, N))
job_info.info = json.dumps({'message': 'Fetched Publications From Elastic Joon ...<br>'
'Creating The Great Matrix ...', 'percentage': 35})
job_info.save()
initial_x = None
for sid_1 in range(N):
uid_1 = mapper.get_uid(sid_1)
ones = 0
for sid_2 in range(N):
if sid_1 == sid_2:
continue
uid_2 = mapper.get_uid(sid_2)
if uid_2 in publications[uid_1].get('references') or uid_1 in publications[uid_2].get('citations'):
links_graph[sid_1][sid_2] = 1
ones += 1
if initial_x is None:
initial_x = sid_1
if ones == 0:
for sid_2 in range(N):
links_graph[sid_1][sid_2] = 1. / N
else:
for sid_2 in range(N):
links_graph[sid_1][sid_2] = links_graph[sid_1][sid_2] / ones * (1-alpha) + alpha / N
# sum = 0
# for sid_2 in range(N):
# sum += links_graph[sid_1][sid_2]
# print(sum)
job_info.info = json.dumps({'message': 'Multiplying ...', 'percentage': 60})
job_info.save()
links_graph = numpy.mat(links_graph)
probability_vector = numpy.mat(numpy.zeros(shape=(1, N)) + 1. / N)
# probability_vector[0, initial_x] = 1.
# prev_prob_vec = probability_vector
for i in range(365):
probability_vector = probability_vector * links_graph
# print("|x * links_graph|", numpy.sum(probability_vector))
probability_vector = probability_vector / numpy.mat(numpy.sum(probability_vector))
# print("|x * links_graph / |||", numpy.sum(probability_vector))
# print(numpy.sum(prev_prob_vec - probability_vector))
# prev_prob_vec = probability_vector
# print(probability_vector)
job_info.info = json.dumps({'message': 'Writing to ElasticSearch ...', 'percentage': 80})
job_info.save()
max_PR = numpy.matrix.max(probability_vector)
for sid in range(N):
uid = mapper.get_uid(sid)
PR = probability_vector[0, sid] / max_PR
es.update(index=index_id, doc_type='publication', id=str(uid), body={"doc": {'PR': PR}})
es.update(index='global-index', doc_type='publication', id=str(uid), body={"doc": {'PR': PR}})
job_info.info = json.dumps({'message': 'Fenitto ...', 'percentage': 100})
job_info.save()
