def __call__(self, key, values_gen):
values = list(values_gen)
version, site, platform, type = key
def result(s_type, c_index, c_size, m_id, message, score):
sortkey = MAX_SIZE - c_size
self.cluster_count += 1
return (sortkey, version, site, platform, s_type, c_index, type, c_size), (m_id, message, score)
c_index = 1
if len(values) == 1:
m_id, message = values[0]
for s_type in (type, None):
yield result(s_type, c_index, 1, m_id, message, 1.0)
else:
corpus = Corpus()
unclustered_opinions = {}
for m_id, message in values:
unclustered_opinions[m_id] = (m_id, message)
corpus.add((m_id, message), str=message, key=m_id)
clusters = corpus.cluster()
for c in clusters:
c_index += 1
rest = [(s["object"], s["similarity"]) for s in c.similars]
c_size = len(rest) + 1
for (m_id, message), score in [(c.primary, 1.0)] + rest:
del unclustered_opinions[m_id]
for s_type in (type, None):
yield result(s_type, c_index, c_size, m_id, message, score)
for m_id, message in unclustered_opinions.values():
c_index += 1
for s_type in (type, None):
yield result(s_type, c_index, 1, m_id, message, 1.0)
def __call__(self, key, values_gen):
values = list(values_gen)
version, site, platform, type = key
def result(s_type, c_index, c_size, m_id, message, score):
sortkey = MAX_SIZE - c_size
self.cluster_count += 1
return \
(sortkey, version, site, platform, s_type, c_index, type, c_size), \
(m_id, message, score)
c_index = 1
if len(values) == 1:
m_id, message = values[0]
for s_type in (type, None):
yield result(s_type, c_index, 1, m_id, message, 1.0)
else:
corpus = Corpus()
unclustered_opinions = {}
for m_id, message in values:
unclustered_opinions[m_id] = (m_id, message)
corpus.add((m_id, message), str=message, key=m_id)
clusters = corpus.cluster()
for c in clusters:
c_index += 1
rest = [(s["object"], s["similarity"]) for s in c.similars]
c_size = len(rest) + 1
for (m_id, message), score in [(c.primary, 1.0)] + rest:
del unclustered_opinions[m_id]
for s_type in (type, None):
yield result(s_type, c_index, c_size, m_id, message,
score)
for m_id, message in unclustered_opinions.values():
c_index += 1
for s_type in (type, None):
yield result(s_type, c_index, 1, m_id, message, 1.0)
def generate_clusters_for(self, err, storage, group):
num_clusters = 0
site_summary = SiteSummary(
pk=self.site_summary_id.next(),
size=len(group.opinion_pks),
issues_count=group.positive_counts[0],
praise_count=group.positive_counts[1],
**group.key
)
storage.save(site_summary)
group_positive = group.key["positive"]
# Handle single-comment case:
if site_summary.size == 1:
opinion = Opinion.objects.get(pk=group.opinion_pks[0])
self.add_singleton_cluster(storage, site_summary, opinion)
return
opinions = Opinion.objects.filter(pk__in=group.opinion_pks)
# Handle cluster case, make one corpus for positive, one for negative.
for positive in (0, 1):
if group_positive is not None and positive != group_positive:
continue
corpus = Corpus()
remaining_opinions = {}
for opinion in opinions:
if opinion.positive != positive:
continue
remaining_opinions[opinion.id] = opinion
corpus.add(opinion, str=unicode(opinion.description))
clusters = corpus.cluster()
for next in clusters:
primary = {"object": next.primary, "similarity": 1.0}
comments = [
Comment(
pk=self.comment_id.next(),
description=doc["object"].description,
opinion_id=doc["object"].id,
score=doc["similarity"],
)
for doc in [primary] + next.similars
]
cluster = Cluster(
pk=self.cluster_id.next(),
site_summary=site_summary,
primary_description=comments[0].description,
primary_comment=None,
positive=positive,
size=len(comments),
)
storage.save(cluster)
for comment in comments:
del remaining_opinions[comment.opinion_id]
comment.cluster = cluster
storage.save(comment)
cluster.primary_comment = comments[0]
cluster.save()
# Add singletons for remaining opinions
for opinion in remaining_opinions.values():
self.add_singleton_cluster(storage, site_summary, opinion)
