def optimise(
self,
sents,
k,
filter,
ix_to_label,
pairwise_sims,
sent_coverages,
avg_sent_sims,
):
alpha = self.a / len(sents)
remaining = set(range(len(sents)))
selected = []
while len(remaining) > 0 and len(selected) < k:
i_to_score = {}
for i in remaining:
summary_indices = selected + [i]
cov = self.compute_summary_coverage(
alpha, summary_indices, sent_coverages, pairwise_sims
)
div = self.compute_summary_diversity(
summary_indices, ix_to_label, avg_sent_sims
)
score = cov + self.div_weight * div
i_to_score[i] = score
if self.plug:
sent_pages = [int(s.article_page) for s in sents]
count_dict = {
i_score[0]: (i_score[1], sent_pages[i_score[0]])
for i_score in i_to_score.items()
}
ranked = plugin.get_combined_1st_rank(
count_dict, page_weight=self.plug, output_one=False
)
else:
ranked = sorted(i_to_score.items(), key=lambda x: x[1], reverse=True)
for i, score in ranked:
s = sents[i]
remaining.remove(i)
if filter and not filter(s):
continue
else:
selected.append(i)
break
return selected
def rank(self, clusters, collection=None, vectorizer=None, plug=False):
if plug:
count_dict = {
i: (
len(cluster),
plugin.get_page_sum_from_cluster(cluster) / len(cluster),
)
for i, cluster in enumerate(clusters)
}
ranked = plugin.get_combined_1st_rank(
count_dict, page_weight=plug, output_one=False
)
return [clusters[i] for i, _ in ranked]
else:
return sorted(clusters, key=len, reverse=True)
def most_mentioned_time_with_influence_rank(self, page_weight=1.0):
count_dict = {}
for a in self.articles:
for s in a.sentences:
if s.time and s.time_level == "d":
count, page_sum = count_dict.get(s.time, (0, 0))
count_dict[s.time] = (count + 1, page_sum + int(s.article_page))
count_dict = {
key: (tuple_[0], tuple_[1] / tuple_[0])
for key, tuple_ in count_dict.items()
}
if count_dict: # equals to "if len(mentioned_times) != 0:"
return plugin.get_combined_1st_rank(count_dict, page_weight)
else:
return None
def summarize(self, sents, k, vectorizer, filter=None):
raw_sents = [s.raw for s in sents]
try:
X = vectorizer.transform(raw_sents)
for i, s in enumerate(sents):
s.vector = X[i]
except:
return None
scores = self.score_sentences(X)
indices = list(range(len(sents)))
if self.plug:
sent_pages = [int(s.article_page) for s in sents]
count_dict = {i: (scores[i], sent_pages[i]) for i in indices}
ranked = plugin.get_combined_1st_rank(
count_dict, page_weight=self.plug, output_one=False
)
else:
ranked = sorted(zip(indices, scores), key=lambda x: x[1], reverse=True)
summary_sents = []
summary_vectors = []
for i, _ in ranked:
if len(summary_sents) >= k:
break
new_x = X[i]
s = sents[i]
is_redundant = False
for x in summary_vectors:
if cosine_similarity(new_x, x)[0, 0] > self.max_sim:
is_redundant = True
break
if filter and not filter(s):
continue
elif is_redundant:
continue
else:
summary_sents.append(sents[i])
summary_vectors.append(new_x)
summary = [s.raw for s in summary_sents]
return summary
def optimise(self, centroid, X, sents, k, filter):
remaining = set(range(len(sents)))
selected = []
while len(remaining) > 0 and len(selected) < k:
if len(selected) > 0:
summary_vector = sparse.vstack([X[i] for i in selected])
summary_vector = sparse.csr_matrix(summary_vector.sum(0))
i_to_score = {}
for i in remaining:
if len(selected) > 0:
new_x = X[i]
new_summary_vector = sparse.vstack([new_x, summary_vector])
new_summary_vector = normalize(new_summary_vector.sum(0))
else:
new_summary_vector = X[i]
score = cosine_similarity(new_summary_vector, centroid)[0, 0]
i_to_score[i] = score
if self.plug:
sent_pages = [int(s.article_page) for s in sents]
count_dict = {
i_score[0]: (i_score[1], sent_pages[i_score[0]])
for i_score in i_to_score.items()
}
ranked = plugin.get_combined_1st_rank(
count_dict, page_weight=self.plug, output_one=False
)
else:
ranked = sorted(i_to_score.items(), key=lambda x: x[1], reverse=True)
for i, _ in ranked:
s = sents[i]
remaining.remove(i)
if filter and not filter(s):
continue
elif self.is_redundant(i, selected, X):
continue
else:
selected.append(i)
break
return selected
def rank_dates(self, collection, plug=False):
date_to_count = collections.defaultdict(int)
if plug:
pages_to_count = collections.defaultdict(int)
for a in collection.articles():
d = a.time.date()
date_to_count[d] += 1
if plug:
pages_to_count[d] += a.page
if plug:
count_dict = {
d: (count, pages_to_count[d] / count)
for d, count in date_to_count.items()
}
ranked = plugin.get_combined_1st_rank(count_dict,
page_weight=plug,
output_one=False)
else:
ranked = sorted(date_to_count.items(),
key=lambda x: x[1],
reverse=True)
return [d for d, _ in ranked]
def rank_dates(self, collection, plug=False):
if plug:
dates, X, pages_to_count = self.extract_features(collection, plug)
else:
dates, X = self.extract_features(collection)
X = normalize(X, norm="l2", axis=0)
if self.method == "classification":
Y = [y[1] for y in self.model["model"].predict_proba(X)]
else:
Y = self.model["model"].predict(X)
if plug:
count_dict = {
d: (Y[i], pages_to_count[d])
for i, d in enumerate(dates)
}
ranked = [
x[0] for x in plugin.get_combined_1st_rank(
count_dict, page_weight=plug, output_one=False)
]
else:
scored = sorted(zip(dates, Y), key=lambda x: x[1], reverse=True)
ranked = [x[0] for x in scored]
return ranked
def rank(self, clusters, collection=None, vectorizer=None, plug=False):
date_to_count = collections.defaultdict(int)
for a in collection.articles():
for s in a.sentences:
d = s.get_date()
if d:
date_to_count[d] += 1
clusters = sorted(clusters, reverse=True, key=len)
def get_count(c):
if plug:
t = c.most_mentioned_time_with_influence_rank(plug)
else:
t = c.most_mentioned_time()
if t:
return date_to_count[t.date()]
else:
return 0
clusters = sorted(
clusters, reverse=True, key=get_count
) # to give each cluster a specific date
if plug:
count_dict = {
i: (
len(cluster),
plugin.get_page_sum_from_cluster(cluster) / len(cluster),
)
for i, cluster in enumerate(clusters)
}
ranked = plugin.get_combined_1st_rank(
count_dict, page_weight=plug, output_one=False
)
return [clusters[i] for i, _ in ranked]
else:
return sorted(clusters, key=len, reverse=True)
