def explore_dataset(dataset, trunc_timelines, time_span_extension):
for collection in dataset.collections:
print('topic:', collection.name)
ref_timelines = [
TilseTimeline(tl.date_to_summaries) for tl in collection.timelines
]
if trunc_timelines:
ref_timelines = truncate_timelines(ref_timelines, collection)
# each collection/topic can have multiple reference timelines
for i, ref_timeline in enumerate(ref_timelines):
ref_dates = sorted(ref_timeline.dates_to_summaries)
# depending on the reference timeline, we set the time range in
# article collection differently
start, end = get_input_time_span(ref_dates, time_span_extension)
collection.start = start
collection.end = end
#utils.plot_date_stats(collection, ref_dates)
l = len(ref_dates)
k = get_average_summary_length(ref_timeline)
print(f'timeline:{i}, k:{k}, l:{l}')
print()
def evaluate(tls_model,
dataset,
result_path,
trunc_timelines=False,
time_span_extension=0,
word_mover_stop_words='nltk'):
results = []
metric = 'align_date_content_costs_many_to_one'
evaluator = tilse_rouge.TimelineRougeEvaluator(
measures=["rouge_1", "rouge_2"])
n_topics = len(dataset.collections)
for i, collection in enumerate(dataset.collections):
ref_timelines = [
TilseTimeline(tl.date_to_summaries) for tl in collection.timelines
]
topic = collection.name
n_ref = len(ref_timelines)
if trunc_timelines:
ref_timelines = data.truncate_timelines(ref_timelines, collection)
for j, ref_timeline in enumerate(ref_timelines):
print(
f'topic {i + 1}/{n_topics}: {topic}, ref timeline {j + 1}/{n_ref}'
)
tls_model.load(ignored_topics=[collection.name])
ref_dates = sorted(ref_timeline.dates_to_summaries)
start, end = data.get_input_time_span(ref_dates,
time_span_extension)
collection.start = start
collection.end = end
# utils.plot_date_stats(collection, ref_dates)
l = len(ref_dates)
k = data.get_average_summary_length(ref_timeline)
pred_timeline_ = tls_model.predict(
collection,
max_dates=l,
max_summary_sents=k,
ref_tl=ref_timeline # only oracles need this
)
print('*** PREDICTED ***')
utils.print_tl(pred_timeline_)
print('timeline done')
pred_timeline = TilseTimeline(pred_timeline_.date_to_summaries)
sys_len = len(pred_timeline.get_dates())
ground_truth = TilseGroundTruth([ref_timeline])
rouge_scores = get_scores(metric, pred_timeline, ground_truth,
evaluator)
date_scores = evaluate_dates(pred_timeline, ground_truth)
wm_scores = get_wordmover_score(pred_timeline,
ground_truth,
word_mover_stop_words,
device='cpu')
dd_scores = date_dist_scores(pred_timeline, ground_truth)
print('sys-len:', sys_len, 'gold-len:', l, 'gold-k:', k)
print('Alignment-based ROUGE:')
pprint(rouge_scores)
print('Date selection:')
pprint(date_scores)
pprint(dd_scores)
print('WordMover scores:')
pprint(wm_scores)
print('-' * 100)
results.append((rouge_scores, date_scores, wm_scores, dd_scores,
pred_timeline_.to_dict()))
print("Running average:")
print(get_average_results(results))
print()
avg_results = get_average_results(results)
print('Average results:')
pprint(avg_results)
output = {
'average': avg_results,
'results': results,
}
utils.write_json(output, result_path)
def evaluate(tls_model,
dataset,
result_path,
trunc_timelines=False,
time_span_extension=0):
results = []
metric = 'align_date_content_costs_many_to_one'
evaluator = rouge.TimelineRougeEvaluator(measures=["rouge_1", "rouge_2"])
n_topics = len(dataset.collections)
ave_cluster = 0
for i, collection in enumerate(dataset.collections):
ref_timelines = [
TilseTimeline(tl.date_to_summaries) for tl in collection.timelines
]
topic = collection.name
n_ref = len(ref_timelines)
# only for entity
if trunc_timelines:
ref_timelines = data.truncate_timelines(ref_timelines, collection)
for j, ref_timeline in enumerate(ref_timelines):
print(
f'topic {i+1}/{n_topics}: {topic}, ref timeline {j+1}/{n_ref}')
tls_model.load(ignored_topics=[collection.name])
ref_dates = sorted(ref_timeline.dates_to_summaries)
#print("data to summaries = {}".format(ref_dates))
start, end = data.get_input_time_span(ref_dates,
time_span_extension)
collection.start = start
collection.end = end
print("name = {} start = {} end = {}".format(topic, start, end))
#utils.plot_date_stats(collection, ref_dates)
l = len(ref_dates)
k = data.get_average_summary_length(ref_timeline)
pred_timeline_, n_clusters = tls_model.predict(
collection,
max_dates=l,
max_summary_sents=k,
ref_tl=ref_timeline # only oracles need this
)
ave_cluster = ave_cluster + n_clusters
# print('*** PREDICTED ***')
# utils.print_tl(pred_timeline_)
print('timeline done')
pred_timeline = TilseTimeline(pred_timeline_.date_to_summaries)
sys_len = len(pred_timeline.get_dates())
ground_truth = TilseGroundTruth([ref_timeline])
rouge_scores = get_scores(metric, pred_timeline, ground_truth,
evaluator)
date_scores = evaluate_dates(pred_timeline, ground_truth)
print('sys-len:', sys_len, 'gold-len:', l, 'gold-k:', k)
print('Alignment-based ROUGE:')
pprint(rouge_scores)
print('Date selection:')
pprint(date_scores)
print('-' * 100)
results.append(
(rouge_scores, date_scores, pred_timeline_.to_dict()))
avg_results = get_average_results(results)
print('Average results:')
pprint(avg_results)
output = {
'average_clusters': ave_cluster / len(dataset.collections),
'average': avg_results,
'results': results,
}
utils.write_json(output, result_path)