def test_covid_round1(self):
topics = pysearch.get_topics('covid_round1_udel')
self.assertEqual(len(topics), 30)
self.assertEqual('coronavirus origin origin COVID-19',
topics[1]['query'])
self.assertEqual(
'coronavirus remdesivir remdesivir effective treatment COVID-19',
topics[30]['query'])
self.assertTrue(isinstance(next(iter(topics.keys())), int))
def test_covid_round2(self):
topics = pysearch.get_topics('covid_round2_udel')
self.assertEqual(len(topics), 35)
self.assertEqual('coronavirus origin origin COVID-19',
topics[1]['query'])
self.assertEqual(
'coronavirus public datasets public datasets COVID-19',
topics[35]['query'])
self.assertTrue(isinstance(next(iter(topics.keys())), int))
def test_msmarco_passage(self):
topics = pysearch.get_topics('msmarco_passage_dev_subset')
self.assertEqual(len(topics), 6980)
self.assertTrue(isinstance(next(iter(topics.keys())), int))
def test_covid_round1(self):
topics = pysearch.get_topics('covid_round1')
self.assertEqual(len(topics), 30)
self.assertEqual('coronavirus origin', topics[1]['query'])
self.assertEqual('coronavirus remdesivir', topics[30]['query'])
self.assertTrue(isinstance(next(iter(topics.keys())), int))
def test_car20(self):
topics = pysearch.get_topics('car17v2.0_benchmarkY1test')
self.assertEqual(len(topics), 2254)
self.assertFalse(isinstance(next(iter(topics.keys())), int))
def test_msmarco_doc(self):
topics = pysearch.get_topics('msmarco_doc_dev')
self.assertEqual(len(topics), 5193)
self.assertTrue(isinstance(next(iter(topics.keys())), int))
def test_robust04(self):
topics = pysearch.get_topics('robust04')
self.assertEqual(len(topics), 250)
self.assertTrue(isinstance(next(iter(topics.keys())), int))
def test_robust04(self):
topics = pysearch.get_topics('robust04')
self.assertEqual(len(topics), 250)
def test_msmarco_doc(self):
topics = pysearch.get_topics('msmarco_doc_dev')
self.assertEqual(len(topics), 5193)
def test_msmarco_passage(self):
topics = pysearch.get_topics('msmarco_passage_dev_subset')
self.assertEqual(len(topics), 6980)
def test_car20(self):
topics = pysearch.get_topics('car17v2.0_benchmarkY1test')
self.assertEqual(len(topics), 2254)
def test_car15(self):
topics = pysearch.get_topics('car17v1.5_benchmarkY1test')
self.assertEqual(len(topics), 2125)
def test_core18(self):
topics = pysearch.get_topics('core18')
self.assertEqual(len(topics), 50)
def __post_init__(self):
self.topics = pysearch.get_topics(self.collection_name)
def test_core18(self):
topics = pysearch.get_topics('core18')
self.assertEqual(len(topics), 50)
self.assertTrue(isinstance(next(iter(topics.keys())), int))
import numpy as np
def make_run_file(file, topics, searcher, w_bm25, w_rnp):
probTrue = np.load('trueProbs_d2v.npy',allow_pickle='TRUE').item()
with open(file, 'w') as runfile:
cnt = 0
print('Running {} queries in total'.format(len(topics)))
for id in topics:
query = topics[id]['title'].encode('utf-8')
hits = searcher.search(query, 10)
for i in range(0, len(hits)):
doc_id = hits[i].docid
bm25_score = hits[i].score
real_news_prob = probTrue[str(doc_id)]
score = w_bm25 * bm25_score + w_rnp * real_news_prob
_ = runfile.write('{} Q0 {} {} {:.6f} Anserini\n'.format(id, hits[i].docid, i+1, score))
cnt += 1
if cnt % 100 == 0:
print(f'{cnt} queries completed')
if __name__ == "__main__":
topics = pysearch.get_topics('robust04')
searcher = pysearch.SimpleSearcher('robust_index')
make_run_file('run.fnc-reranker.txt', topics , searcher, 0.5, 0.5)
from es import ElasticSearcher
from elasticsearch import Elasticsearch
from pprint import pprint
from pyserini.search import pysearch
from tqdm.autonotebook import tqdm
# open topic xml
# for each topic
# - run query with es
# - store result
print(text2art('COVID-19 Browser'))
collection_name = 'covid_round3_udel'
embedder = Embedder()
es_searcher = ElasticSearcher(index_name='lucene-index-cord19-2020-05-19-bm25',
size=1000)
topics = pysearch.get_topics(collection_name)
bar = tqdm(topics)
dfs = []
for topicid in bar:
query = topics[topicid]['query']
bar.set_description(f"[{topicid}]{query[:40]}...")
query_emb = embedder([query])[0].tolist()
res = es_searcher(query_emb, topic=topicid)
df = pd.DataFrame.from_records(res.json)
df['topicid'] = topicid
dfs.append(df)
final_df = pd.concat(dfs)
