def run(self):
# read events
archive_events = []
active_events = []
date = datetime.datetime(int(self.archivedate[:4]),
int(self.archivedate[4:6]),
int(self.archivedate[6:8]))
print('Reading events')
with open(self.in_events().path, 'r', encoding='utf-8') as file_in:
eventdicts = json.loads(file_in.read())
for i, ed in enumerate(eventdicts):
eventobj = event.Event()
eventobj.import_eventdict(ed)
if eventobj.datetime == date:
archive_events.append(eventobj)
else:
active_events.append(eventobj)
# write archive
print('Writing archive')
out_archive_events = [
event.return_dict(txt=False) for event in archive_events
]
with open(self.out_archived().path, 'w', encoding='utf-8') as file_out:
json.dump(out_archive_events, file_out)
# write active events
print('Writing active events')
out_active_events = [
event.return_dict(txt=False) for event in active_events
]
with open(self.out_active_events().path, 'w',
encoding='utf-8') as file_out:
json.dump(out_active_events, file_out)
def run(self):
# read in events
print('Reading in events')
with open(self.in_deduplicated_events().path, 'r',
encoding='utf-8') as file_in:
eventdicts = json.loads(file_in.read())
event_objs = []
for ed in eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed)
event_objs.append(eventobj)
# initialize event enhancer
print('Enhancing events')
enhancer = event_enhancer.EventEnhancer()
enhancer.set_events(event_objs)
enhancer.enhance()
enhanced_events = enhancer.return_events()
# write deduplicated
out_enhanced_events = [
event.return_dict() for event in enhanced_events
]
with open(self.out_enhanced_events().path, 'w',
encoding='utf-8') as file_out:
json.dump(out_enhanced_events, file_out)
def run(self):
# read in events
with open(self.in_enhanced_events().path, 'r', encoding = 'utf-8') as file_in:
eventdicts = json.loads(file_in.read())
event_objs = []
for ed in eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed)
event_objs.append(eventobj)
# initialize event merger
print('Merging; number of events at start:',len(event_objs))
overlap_threshold = float(self.overlap_threshold)
merger = event_merger.EventMerger()
merger.add_events(event_objs)
merger.find_merges(overlap_threshold)
events_merged = merger.return_events()
print('Merging again; current number of events:',len(events_merged))
merger2 = event_merger.EventMerger()
merger2.add_events(events_merged)
merger2.find_merges(overlap_threshold)
events_merged_final = merger2.return_events()
print('Done. number of events after merge:',len(events_merged_final))
# write merged
out_merged_events = [event.return_dict(txt=False) for event in events_merged_final]
with open(self.out_merged_events().path,'w',encoding='utf-8') as file_out:
json.dump(out_merged_events,file_out)
def run(self):
# read in events
print('Reading in events')
with open(self.in_merged_events().path, 'r',
encoding='utf-8') as file_in:
eventdicts = json.loads(file_in.read())
event_objs = []
for ed in eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed, txt=False)
event_objs.append(eventobj)
print('Reading in citylist')
# read in citylist
with open(self.citylist, 'r', encoding='utf-8') as file_in:
citylist = [
line.strip() for line in file_in.read().strip().split('\n')
]
# initialize event filter
print('Filtering; number of events at start:', len(event_objs))
filter = event_filter.EventFilter()
filter.add_events(event_objs)
filter.apply_filter(citylist)
events_filtered = filter.return_events()
print('Done. number of events after filter:', len(events_filtered))
# write filter
out_filtered_events = [
event.return_dict(txt=False) for event in events_filtered
]
with open(self.out_filtered_events().path, 'w',
encoding='utf-8') as file_out:
json.dump(out_filtered_events, file_out)
def run(self):
# if directory does not exist, create directory
if not os.path.isdir(self.out_eventdir().path):
self.setup_output_dir(self.out_eventdir().path)
# collect tweet files
end_date_year = self.end_date[:4]
end_date_month = self.end_date[4:6]
end_date_day = self.end_date[6:]
last_date = datetime.date(int(end_date_year), int(end_date_month),
int(end_date_day))
first_date = last_date - datetime.timedelta(days=self.window_size - 1)
last_tweetfile = self.in_tweetdir(
).path + '/' + end_date_year + end_date_month + '/' + end_date_year + end_date_month + end_date_day + '-23.out.dateref.cityref.entity.json'
days_tweetfiles = helpers.return_tweetfiles_window(
last_tweetfile, self.window_size - 1)
tweetfiles = []
for day in days_tweetfiles:
tweetfiles.extend([
filename for filename in glob.glob(self.in_tweetdir().path +
'/' + day + '*')
])
print('Reading in citylist')
# read in citylist
with open(self.citylist, 'r', encoding='utf-8') as file_in:
citylist = [
line.strip() for line in file_in.read().strip().split('\n')
]
# extract events
er = event_ranker.EventRanker()
# read in tweets
print('Reading in tweets')
for tweetfile in tweetfiles:
date = helpers.return_date_entitytweetfile(tweetfile)
with open(tweetfile, 'r', encoding='utf-8') as file_in:
tweetdicts = json.loads(file_in.read())
# format as tweet objects
for td in tweetdicts:
if not (td['refdates'] == {} and td['entities'] == {}):
tweetobj = tweet.Tweet()
tweetobj.import_tweetdict(td)
er.add_tweet(tweetobj)
er.tweet_count += 1
# extract events
print('Performing event extraction')
er.extract_events(self.minimum_event_mentions, self.cut_off)
filter = event_filter.EventFilter()
filter.add_events(er.events)
filter.apply_filter(citylist)
events_filtered = filter.return_events()
print('Done. Extracted', len(events_filtered), 'events')
# write to file
outevents = [event.return_dict() for event in events_filtered]
with open(self.out_events().path, 'w', encoding='utf-8') as file_out:
json.dump(outevents, file_out)
def run(self):
# read in events
with open(self.in_events().path, 'r', encoding='utf-8') as file_in:
eventdicts = json.loads(file_in.read())
event_objs = []
for ed in eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed)
event_objs.append(eventobj)
# initialize event deduplicator
similarity_threshold = float(self.similarity_threshold)
print('Deduplicating; number of events at start:', len(event_objs))
deduplicator = event_deduplicator.EventDeduplicator()
deduplicator.set_events(event_objs)
deduplicator.deduplicate_events(similarity_threshold)
deduplicated_events = deduplicator.return_events()
print('Done. number of events after deduplication:',
len(deduplicated_events))
# write deduplicated
out_deduplicated_events = [
event.return_dict() for event in deduplicated_events
]
with open(self.out_deduplicated_events().path, 'w',
encoding='utf-8') as file_out:
json.dump(out_deduplicated_events, file_out)
def run(self):
# read in new events
with open(self.in_events().path, 'r', encoding='utf-8') as file_in:
new_eventdicts = json.loads(file_in.read())
new_event_objs = []
for ed in new_eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed, txt=False)
new_event_objs.append(eventobj)
earliest_date = min([event.datetime for event in new_event_objs])
# read in current events
with open(self.current_events, 'r', encoding='utf-8') as file_in:
current_eventdicts = json.loads(file_in.read())
current_event_objs = []
current_event_objs_candidates = []
for ed in current_eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed, txt=False)
if eventobj.datetime >= earliest_date:
current_event_objs_candidates.append(eventobj)
else:
current_event_objs.append(eventobj)
# initialize event merger
merger = event_merger.EventMerger()
merger.add_events(current_event_objs_candidates)
# merge before integration
print(
'Merging new events before integration; number of events at start:',
len(new_event_objs))
overlap_threshold = float(self.overlap_threshold)
premerger = event_merger.EventMerger()
premerger.add_events(new_event_objs)
premerger.find_merges(overlap_threshold)
new_events_merged = premerger.return_events()
print('Done. New events after merge:', len(new_events_merged))
# integrate each event into the current ones
print('Starting integrating new events; number of current events:',
len(current_event_objs))
for new_event in new_events_merged:
merger.find_merge(new_event, overlap_threshold)
# write merged
integrated_events = merger.return_events() + current_event_objs
print('Done. Number of events after integration:',
len(integrated_events))
out_integrated_events = [
event.return_dict(txt=False) for event in integrated_events
]
with open(self.out_integrated_events().path, 'w',
encoding='utf-8') as file_out:
json.dump(out_integrated_events, file_out)
def run(self):
# initiate directory
self.setup_output_dir(self.out_archivedir().path)
# read events
datebound = datetime.now() - datetime.timedelta(days=100)
date_events = defaultdict(list)
active_events = []
print('Reading events')
with open(self.in_events().path, 'r', encoding='utf-8') as file_in:
eventdicts = json.loads(file_in.read())
for i, ed in enumerate(eventdicts):
eventobj = event.Event()
eventobj.import_eventdict(ed)
if eventobj.datetime < datebound:
date_events[''.join(
str(eventobj.datetime).split()[0].split('-'))].append(
eventobj)
else:
active_events.append(eventobj)
# write archives
print('Writing archives')
for date in sorted(list(date_events.keys())):
print(date)
events = date_events[date]
out_events = [event.return_dict(txt=False) for event in events]
outfile = self.out_archivedir().path + '/events_' + date + '.json'
with open(outfile, 'w', encoding='utf-8') as file_out:
json.dump(out_events, file_out)
# write active events
print('Writing active events')
out_active_events = [
event.return_dict(txt=False) for event in active_events
]
with open(self.out_active_events().path, 'w',
encoding='utf-8') as file_out:
json.dump(out_active_events, file_out)
def run(self):
# collect all event files with extension '.enhanced'
enhanced_events = glob.glob(self.in_eventdir().path + '/*.enhanced')
# initialize
merger = event_merger.EventMerger()
overlap_threshold = float(self.overlap_threshold)
# for each event file
for eventfile in enhanced_events:
print('Reading', eventfile)
with open(eventfile, 'r', encoding='utf-8') as file_in:
current_eventdicts = json.loads(file_in.read())
new_event_objs = []
for ed in current_eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed)
new_event_objs.append(eventobj)
# merge before integration
print(
'Merging new events before integration; number of events at start:',
len(new_event_objs))
premerger = event_merger.EventMerger()
premerger.add_events(new_event_objs)
premerger.find_merges(overlap_threshold)
new_events_merged = premerger.return_events()
print('Done. New events after merge:', len(new_events_merged))
if len(merger.events) == 0:
merger.add_events(new_events_merged)
else:
# integrate each event into the current ones
print(
'Starting integrating new events; number of current events:',
len(merger.events))
for new_event in new_events_merged:
merger.find_merge(new_event, overlap_threshold)
# write merged
integrated_events = merger.return_events()
print('Done. Number of events after integration:',
len(integrated_events))
out_integrated_events = [
event.return_dict() for event in integrated_events
]
with open(self.out_integrated_events().path, 'w',
encoding='utf-8') as file_out:
json.dump(out_integrated_events, file_out)
def run(self):
# read prediction data
with open(self.in_predictiondir().path + '/events_meta.txt','r',encoding='utf=8') as file_in:
meta = file_in.read().strip().split('\n')
with open(self.in_predictiondir().path + '/events_text.predictions.txt','r',encoding='utf=8') as file_in:
predictions = file_in.read().strip().split('\n')
with open(self.in_predictiondir().path + '/events_text.full_predictions.txt','r',encoding='utf=8') as file_in:
lines = file_in.read().strip().split('\n')
label_order = lines[0].split('\t')
full_predictions = [line.split('\t') for line in lines[1:]]
print('Meta',len(meta))
print('Predictions',len(predictions))
print('Full predictions',len(full_predictions))
# read in events
print('Reading in events')
with open(self.in_events().path, 'r', encoding = 'utf-8') as file_in:
eventdicts = json.loads(file_in.read())
event_objs = []
for ed in eventdicts:
eventobj = event.Event()
eventobj.import_eventdict(ed,txt=self.text)
event_objs.append(eventobj)
# index events
id_event = {}
for eo in event_objs:
id_event[eo.mongo_id] = eo
# for each prediction
for i,mid in enumerate(meta):
prediction = predictions[i]
prediction_score = dict(zip(label_order,full_predictions[i]))
eo = id_event[mid]
eo.eventtype = prediction
eo.eventtype_scores = prediction_score
# write output
out_updated_events = [event.return_dict(txt=self.text) for event in event_objs]
with open(self.out_updated_events().path,'w',encoding='utf-8') as file_out:
json.dump(out_updated_events,file_out)
def run(self):
# create directory
self.setup_output_dir(self.out_eventdir().path)
# set dates
end_date_year = self.end_date[:4]
end_date_month = self.end_date[4:6]
end_date_day = self.end_date[6:]
last_date = datetime.date(int(end_date_year), int(end_date_month),
int(end_date_day))
start_date_year = self.start_date[:4]
start_date_month = self.start_date[4:6]
start_date_day = self.start_date[6:]
first_date = datetime.date(int(start_date_year), int(start_date_month),
int(start_date_day))
print('Reading in citylist')
# read in citylist
with open(self.citylist, 'r', encoding='utf-8') as file_in:
citylist = [
line.strip() for line in file_in.read().strip().split('\n')
]
# perform event extraction on first tweet window
print('Reading in first window of tweets')
cursor_date = first_date
date_tweetobjects = defaultdict(list)
date_tweetcounts = defaultdict(int)
window_dates = []
while cursor_date < first_date + datetime.timedelta(
days=self.window_size):
print(cursor_date)
tweetfiles = [
filename for filename in
glob.glob(self.in_tweetdir().path + '/' +
helpers.return_timeobj_date(cursor_date) + '*')
]
for tweetfile in tweetfiles:
# read in tweets
with open(tweetfile, 'r', encoding='utf-8') as file_in:
tweetdicts = json.loads(file_in.read())
date_tweetcounts[cursor_date] += len(tweetdicts)
for td in tweetdicts:
if not (td['refdates'] == {} and td['entities'] == {}):
tweetobj = tweet.Tweet()
tweetobj.import_tweetdict(td)
date_tweetobjects[cursor_date].append(tweetobj)
window_dates.append(cursor_date)
cursor_date += datetime.timedelta(days=1)
# start event extraction
print('Loading tweets into event extractor')
er = event_ranker.EventRanker()
for date in window_dates:
for tweetobject in date_tweetobjects[date]:
er.add_tweet(tweetobject)
er.tweet_count += date_tweetcounts[date]
print('Performing event extraction')
er.extract_events(self.minimum_event_mentions, self.cut_off)
filter = event_filter.EventFilter()
filter.add_events(er.events)
filter.apply_filter(citylist)
events_filtered = filter.return_events()
print('Done. Extracted', len(events_filtered), 'events')
# write to file
outevents = [event.return_dict() for event in events_filtered]
with open(self.out_eventdir().path + '/' +
str(cursor_date - datetime.timedelta(days=1)).replace(
'-', '') + '.events',
'w',
encoding='utf-8') as file_out:
json.dump(outevents, file_out)
# slide window forward and perform event extraction until last date
print('Starting slider')
window_tail = first_date
window_head = cursor_date - datetime.timedelta(days=1)
while window_head <= last_date:
print('Discarding and collecting tweets')
end_slider = window_head + datetime.timedelta(days=self.slider)
while window_head < end_slider:
# remove tweets of tail
print('Deleting records for old tail', window_tail)
del date_tweetobjects[window_tail]
del date_tweetcounts[window_tail]
window_tail = window_tail + datetime.timedelta(days=1)
window_head = window_head + datetime.timedelta(days=1)
print('Collecting tweets for new head', window_head)
tweetfiles = [
filename for filename in
glob.glob(self.in_tweetdir().path + '/' +
helpers.return_timeobj_date(window_head) + '*')
]
for tweetfile in tweetfiles:
# read in tweets
with open(tweetfile, 'r', encoding='utf-8') as file_in:
tweetdicts = json.loads(file_in.read())
date_tweetcounts[window_head] += len(tweetdicts)
for td in tweetdicts:
if not (td['refdates'] == {} and td['entities'] == {}):
tweetobj = tweet.Tweet()
tweetobj.import_tweetdict(td)
date_tweetobjects[window_head].append(tweetobj)
# add tweets to event ranker
print('Loading tweets into event extractor for', window_tail, '-',
window_head)
er = event_ranker.EventRanker()
window_dates = helpers.return_daterange(window_tail,
self.window_size)
for date in window_dates:
for tweetobject in date_tweetobjects[date]:
er.add_tweet(tweetobject)
er.tweet_count += date_tweetcounts[date]
print('Performing event extraction')
er.extract_events(self.minimum_event_mentions, self.cut_off)
filter = event_filter.EventFilter()
filter.add_events(er.events)
filter.apply_filter(citylist)
events_filtered = filter.return_events()
print('Done. Extracted', len(events_filtered), 'events')
# write to file
outevents = [event.return_dict() for event in events_filtered]
with open(self.out_eventdir().path + '/' +
str(window_head).replace('-', '') + '.events',
'w',
encoding='utf-8') as file_out:
json.dump(outevents, file_out)
