def add_singleton_cluster(self, storage, site_summary, opinion):
cluster = Cluster(
pk=self.cluster_id.next(),
site_summary=site_summary,
primary_description=opinion.description,
primary_comment=None,
positive=opinion.positive,
size=1,
)
storage.save(cluster)
comment = Comment(
pk=self.comment_id.next(),
description=opinion.description,
opinion_id=opinion.id,
cluster=cluster,
score=1.0,
)
storage.save(comment)
cluster.primary_comment = comment
storage.save(cluster)
def make_clusters(summary, type, numcomments):
"""Create a bunch of clusters for the given summary."""
numcreated = 0
for csize in [NUM_PRAISE - NUM_ISSUES, NUM_ISSUES]:
if numcreated >= numcomments:
break
cluster = Cluster(site_summary=summary, size=csize)
for i in xrange(csize):
if i == 0:
cluster.save()
c = make_comment(cluster, csize, i, type)
if i == 0:
cluster.primary_description = c.description
cluster.primary_comment = c
cluster.save()
numcreated += csize
def make_clusters(summary, type, numcomments):
"""Create a bunch of clusters for the given summary."""
numcreated = 0
for csize in [NUM_PRAISE - NUM_ISSUES, NUM_ISSUES]:
if numcreated >= numcomments: break
cluster = Cluster(site_summary=summary, size=csize)
for i in xrange(csize):
if i == 0:
cluster.save()
c = make_comment(cluster, csize, i, type)
if i == 0:
cluster.primary_description = c.description
cluster.primary_comment = c
cluster.save()
numcreated += csize
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)
