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| # | |
| # Pyserini: Reproducible IR research with sparse and dense representations | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| """Integration tests for ANCE and ANCE PRF using on-the-fly query encoding.""" | |
| import os | |
| import socket | |
| import unittest | |
| from integrations.utils import clean_files, run_command, parse_score, parse_score_qa, parse_score_msmarco | |
| from pyserini.search import QueryEncoder | |
| from pyserini.search import get_topics | |
| class TestSearchIntegration(unittest.TestCase): | |
| def setUp(self): | |
| self.temp_files = [] | |
| self.threads = 16 | |
| self.batch_size = 256 | |
| self.rocchio_alpha = 0.4 | |
| self.rocchio_beta = 0.6 | |
| # Hard-code larger values for internal servers | |
| if socket.gethostname().startswith('damiano') or socket.gethostname().startswith('orca'): | |
| self.threads = 36 | |
| self.batch_size = 144 | |
| def test_ance_encoded_queries(self): | |
| encoded = QueryEncoder.load_encoded_queries('ance-msmarco-passage-dev-subset') | |
| topics = get_topics('msmarco-passage-dev-subset') | |
| for t in topics: | |
| self.assertTrue(topics[t]['title'] in encoded.embedding) | |
| encoded = QueryEncoder.load_encoded_queries('ance-dl19-passage') | |
| topics = get_topics('dl19-passage') | |
| for t in topics: | |
| self.assertTrue(topics[t]['title'] in encoded.embedding) | |
| encoded = QueryEncoder.load_encoded_queries('ance-dl20') | |
| topics = get_topics('dl20') | |
| for t in topics: | |
| self.assertTrue(topics[t]['title'] in encoded.embedding) | |
| def test_msmarco_passage_ance_avg_prf_otf(self): | |
| output_file = 'test_run.dl2019.ance.avg-prf.otf.trec' | |
| self.temp_files.append(output_file) | |
| cmd1 = f'python -m pyserini.search.faiss --topics dl19-passage \ | |
| --index msmarco-passage-ance-bf \ | |
| --encoder castorini/ance-msmarco-passage \ | |
| --batch-size {self.batch_size} \ | |
| --threads {self.threads} \ | |
| --output {output_file} \ | |
| --prf-depth 3 \ | |
| --prf-method avg' | |
| cmd2 = f'python -m pyserini.eval.trec_eval -l 2 -m map dl19-passage {output_file}' | |
| status = os.system(cmd1) | |
| stdout, stderr = run_command(cmd2) | |
| score = parse_score(stdout, 'map') | |
| self.assertEqual(status, 0) | |
| self.assertAlmostEqual(score, 0.4247, delta=0.0001) | |
| def test_msmarco_passage_ance_rocchio_prf_otf(self): | |
| output_file = 'test_run.dl2019.ance.rocchio-prf.otf.trec' | |
| self.temp_files.append(output_file) | |
| cmd1 = f'python -m pyserini.search.faiss --topics dl19-passage \ | |
| --index msmarco-passage-ance-bf \ | |
| --encoder castorini/ance-msmarco-passage \ | |
| --batch-size {self.batch_size} \ | |
| --threads {self.threads} \ | |
| --output {output_file} \ | |
| --prf-depth 5 \ | |
| --prf-method rocchio \ | |
| --rocchio-topk 5 \ | |
| --threads {self.threads} \ | |
| --rocchio-alpha {self.rocchio_alpha} \ | |
| --rocchio-beta {self.rocchio_beta}' | |
| cmd2 = f'python -m pyserini.eval.trec_eval -l 2 -m map dl19-passage {output_file}' | |
| status = os.system(cmd1) | |
| stdout, stderr = run_command(cmd2) | |
| score = parse_score(stdout, 'map') | |
| self.assertEqual(status, 0) | |
| self.assertAlmostEqual(score, 0.4211, delta=0.0001) | |
| def test_msmarco_doc_ance_bf_otf(self): | |
| output_file = 'test_run.msmarco-doc.passage.ance-maxp.otf.txt' | |
| self.temp_files.append(output_file) | |
| cmd1 = f'python -m pyserini.search.faiss --topics msmarco-doc-dev \ | |
| --index msmarco-doc-ance-maxp-bf \ | |
| --encoder castorini/ance-msmarco-doc-maxp \ | |
| --output {output_file}\ | |
| --hits 1000 \ | |
| --max-passage \ | |
| --max-passage-hits 100 \ | |
| --output-format msmarco \ | |
| --batch-size {self.batch_size} \ | |
| --threads {self.threads}' | |
| cmd2 = f'python -m pyserini.eval.msmarco_doc_eval --judgments msmarco-doc-dev --run {output_file}' | |
| status = os.system(cmd1) | |
| stdout, stderr = run_command(cmd2) | |
| score = parse_score_msmarco(stdout, 'MRR @100') | |
| self.assertEqual(status, 0) | |
| # We get a small difference, 0.3794 on macOS. | |
| self.assertAlmostEqual(score, 0.3796, delta=0.0002) | |
| def test_msmarco_doc_ance_bf_encoded_queries(self): | |
| encoder = QueryEncoder.load_encoded_queries('ance_maxp-msmarco-doc-dev') | |
| topics = get_topics('msmarco-doc-dev') | |
| for t in topics: | |
| self.assertTrue(topics[t]['title'] in encoder.embedding) | |
| def test_nq_test_ance_bf_otf(self): | |
| output_file = 'test_run.ance.nq-test.multi.bf.otf.trec' | |
| retrieval_file = 'test_run.ance.nq-test.multi.bf.otf.json' | |
| self.temp_files.extend([output_file, retrieval_file]) | |
| cmd1 = f'python -m pyserini.search.faiss --topics dpr-nq-test \ | |
| --index wikipedia-ance-multi-bf \ | |
| --encoder castorini/ance-dpr-question-multi \ | |
| --output {output_file} \ | |
| --batch-size {self.batch_size} --threads {self.threads}' | |
| cmd2 = f'python -m pyserini.eval.convert_trec_run_to_dpr_retrieval_run --topics dpr-nq-test \ | |
| --index wikipedia-dpr \ | |
| --input {output_file} \ | |
| --output {retrieval_file}' | |
| cmd3 = f'python -m pyserini.eval.evaluate_dpr_retrieval --retrieval {retrieval_file} --topk 20' | |
| status1 = os.system(cmd1) | |
| status2 = os.system(cmd2) | |
| stdout, stderr = run_command(cmd3) | |
| score = parse_score_qa(stdout, 'Top20') | |
| self.assertEqual(status1, 0) | |
| self.assertEqual(status2, 0) | |
| self.assertAlmostEqual(score, 0.8224, places=4) | |
| def test_nq_test_ance_encoded_queries(self): | |
| encoder = QueryEncoder.load_encoded_queries('dpr_multi-nq-test') | |
| topics = get_topics('dpr-nq-test') | |
| for t in topics: | |
| self.assertTrue(topics[t]['title'] in encoder.embedding) | |
| def test_trivia_test_ance_bf_otf(self): | |
| output_file = 'test_run.ance.trivia-test.multi.bf.otf.trec' | |
| retrieval_file = 'test_run.ance.trivia-test.multi.bf.otf.json' | |
| self.temp_files.extend([output_file, retrieval_file]) | |
| cmd1 = f'python -m pyserini.search.faiss --topics dpr-trivia-test \ | |
| --index wikipedia-ance-multi-bf \ | |
| --encoder castorini/ance-dpr-question-multi \ | |
| --output {output_file} \ | |
| --batch-size {self.batch_size} --threads {self.threads}' | |
| cmd2 = f'python -m pyserini.eval.convert_trec_run_to_dpr_retrieval_run --topics dpr-trivia-test \ | |
| --index wikipedia-dpr \ | |
| --input {output_file} \ | |
| --output {retrieval_file}' | |
| cmd3 = f'python -m pyserini.eval.evaluate_dpr_retrieval --retrieval {retrieval_file} --topk 20' | |
| status1 = os.system(cmd1) | |
| status2 = os.system(cmd2) | |
| stdout, stderr = run_command(cmd3) | |
| score = parse_score_qa(stdout, 'Top20') | |
| self.assertEqual(status1, 0) | |
| self.assertEqual(status2, 0) | |
| self.assertAlmostEqual(score, 0.8010, places=4) | |
| def test_trivia_test_ance_encoded_queries(self): | |
| encoder = QueryEncoder.load_encoded_queries('dpr_multi-trivia-test') | |
| topics = get_topics('dpr-trivia-test') | |
| for t in topics: | |
| self.assertTrue(topics[t]['title'] in encoder.embedding) | |
| def tearDown(self): | |
| clean_files(self.temp_files) | |
| if __name__ == '__main__': | |
| unittest.main() | |