def phase2(self, basic_events):
print('Phase 2...')
# create the event graph (directed) and the redundancy graph (undirected)
self.event_graph = nx.DiGraph(name='Event graph')
self.redundancy_graph = nx.Graph(name='Redundancy graph')
refined_events = []
for basic_event in basic_events:
main_word = basic_event[2]
candidate_words = self.corpus.cooccurring_words(
basic_event, self.rel_words_per_event)
main_word_freq = self.corpus.global_freq[
self.corpus.vocabulary[main_word], :].toarray()
main_word_freq = main_word_freq[0, :]
related_words = []
# identify candidate words based on co-occurrence
if candidate_words is not None:
for candidate_word in candidate_words:
candidate_word_freq = self.corpus.global_freq[
self.corpus.vocabulary[candidate_word], :].toarray()
candidate_word_freq = candidate_word_freq[0, :]
# compute correlation and filter according to theta
weight = (st.erdem_correlation(
main_word_freq, candidate_word_freq) + 1) / 2
if weight >= self.theta:
related_words.append((candidate_word, weight))
# if len(related_words) > 1:
# I also removed a tab. The following lines were inside the "if candidate_words is not None:"
else:
print("no related words")
refined_event = (basic_event[0], basic_event[1], main_word,
related_words, basic_event[3])
if self.update_graphs(
refined_event
): # check if this event is distinct from those already stored in the event graph
refined_events.append(refined_event)
else:
print("Different main word but same related words")
# merge redundant events and save the result
self.events = self.merge_redundant_events(refined_events)
return self.events
def phase2(self, basic_events):
print('Phase 2...')
# sort the events detected during phase 1 according to their magnitude of impact
basic_events.sort(key=lambda tup: tup[0], reverse=True)
# create the event graph (directed) and the redundancy graph (undirected)
self.event_graph = nx.DiGraph(name='Event graph')
self.redundancy_graph = nx.Graph(name='Redundancy graph')
i = 0
unique_events = 0
refined_events = []
# phase 2 goes on until the top k (distinct) events have been identified
while unique_events < self.k and i < len(basic_events):
basic_event = basic_events[i]
main_word = basic_event[2]
candidate_words = self.corpus.cooccurring_words(
basic_event, self.p)
main_word_freq = self.corpus.global_freq[
self.corpus.vocabulary[main_word], :].toarray()
main_word_freq = main_word_freq[0, :]
related_words = []
# identify candidate words based on co-occurrence
if candidate_words is not None:
for candidate_word in candidate_words:
candidate_word_freq = self.corpus.global_freq[
self.corpus.vocabulary[candidate_word], :].toarray()
candidate_word_freq = candidate_word_freq[0, :]
# compute correlation and filter according to theta
weight = (st.erdem_correlation(
main_word_freq, candidate_word_freq) + 1) / 2
if weight >= self.theta:
related_words.append((candidate_word, weight))
if len(related_words) > 1:
refined_event = (basic_event[0], basic_event[1], main_word,
related_words, basic_event[3])
# check if this event is distinct from those already stored in the event graph
if self.update_graphs(refined_event):
refined_events.append(refined_event)
unique_events += 1
i += 1
# merge redundant events and save the result
self.events = self.merge_redundant_events(refined_events)
def phase2(self, basic_events):
print('Phase 2...')
# sort the events detected during phase 1 according to their magnitude of impact
basic_events.sort(key=lambda tup: tup[0], reverse=True)
# create the event graph (directed) and the redundancy graph (undirected)
self.event_graph = nx.DiGraph(name='Event graph')
self.redundancy_graph = nx.Graph(name='Redundancy graph')
i = 0
unique_events = 0
refined_events = []
# phase 2 goes on until the top k (distinct) events have been identified
while unique_events < self.k and i < len(basic_events):
basic_event = basic_events[i]
main_word = basic_event[2]
candidate_words = self.corpus.cooccurring_words(basic_event, self.p)
main_word_freq = self.corpus.global_freq[self.corpus.vocabulary[main_word], :].toarray()
main_word_freq = main_word_freq[0, :]
related_words = []
# identify candidate words based on co-occurrence
if candidate_words is not None:
for candidate_word in candidate_words:
candidate_word_freq = self.corpus.global_freq[self.corpus.vocabulary[candidate_word], :].toarray()
candidate_word_freq = candidate_word_freq[0, :]
# compute correlation and filter according to theta
weight = (st.erdem_correlation(main_word_freq, candidate_word_freq) + 1) / 2
if weight >= self.theta:
related_words.append((candidate_word, weight))
if len(related_words) > 1:
refined_event = (basic_event[0], basic_event[1], main_word, related_words, basic_event[3])
# check if this event is distinct from those already stored in the event graph
if self.update_graphs(refined_event):
refined_events.append(refined_event)
unique_events += 1
i += 1
# merge redundant events and save the result
self.events = self.merge_redundant_events(refined_events)
