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#
# Pyserini: Python interface to the Anserini IR toolkit built on Lucene
#
# 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.
#
"""
This module provides Pyserini's Python ltr search interface on MS MARCO passage. The main entry point is the ``MsmarcoPassageLtrSearcher``
class.
"""
import logging
import multiprocessing
import time
import os
from tqdm import tqdm
import pickle
from pyserini.index.lucene import IndexReader
from pyserini.search.lucene import LuceneSearcher
from pyserini.util import get_cache_home
from pyserini.search.lucene.ltr._base import *
logger = logging.getLogger(__name__)
class MsmarcoLtrSearcher:
def __init__(self, model: str, ibm_model:str, index:str, data: str, prebuilt: bool, topic: str):
#msmarco-ltr-passage
self.model = model
self.ibm_model = ibm_model
if prebuilt:
self.lucene_searcher = LuceneSearcher.from_prebuilt_index(index)
index_directory = os.path.join(get_cache_home(), 'indexes')
if data == 'passage':
index_path = os.path.join(index_directory, 'index-msmarco-passage-ltr-20210519-e25e33f.a5de642c268ac1ed5892c069bdc29ae3')
else:
index_path = os.path.join(index_directory, 'index-msmarco-doc-per-passage-ltr-20211031-33e4151.bd60e89041b4ebbabc4bf0cfac608a87')
self.index_reader = IndexReader.from_prebuilt_index(index)
else:
index_path = index
self.index_reader = IndexReader(index)
self.fe = FeatureExtractor(index_path, max(multiprocessing.cpu_count()//2, 1))
self.data = data
def add_fe(self):
#self.fe.add(RunList('collections/msmarco-ltr-passage/run.monot5.run_list.whole.trec','t5'))
#self.fe.add(RunList('../bert.whole.doc.trec','bert'))
for qfield, ifield in [('analyzed', 'contents'),
('text_unlemm', 'text_unlemm'),
('text_bert_tok', 'text_bert_tok')]:
print(qfield, ifield)
self.fe.add(BM25Stat(SumPooler(), k1=2.0, b=0.75, field=ifield, qfield=qfield))
self.fe.add(BM25Stat(AvgPooler(), k1=2.0, b=0.75, field=ifield, qfield=qfield))
self.fe.add(BM25Stat(MedianPooler(), k1=2.0, b=0.75, field=ifield, qfield=qfield))
self.fe.add(BM25Stat(MaxPooler(), k1=2.0, b=0.75, field=ifield, qfield=qfield))
self.fe.add(BM25Stat(MinPooler(), k1=2.0, b=0.75, field=ifield, qfield=qfield))
self.fe.add(BM25Stat(MaxMinRatioPooler(), k1=2.0, b=0.75, field=ifield, qfield=qfield))
self.fe.add(LmDirStat(SumPooler(), mu=1000, field=ifield, qfield=qfield))
self.fe.add(LmDirStat(AvgPooler(), mu=1000, field=ifield, qfield=qfield))
self.fe.add(LmDirStat(MedianPooler(), mu=1000, field=ifield, qfield=qfield))
self.fe.add(LmDirStat(MaxPooler(), mu=1000, field=ifield, qfield=qfield))
self.fe.add(LmDirStat(MinPooler(), mu=1000, field=ifield, qfield=qfield))
self.fe.add(LmDirStat(MaxMinRatioPooler(), mu=1000, field=ifield, qfield=qfield))
self.fe.add(NormalizedTfIdf(field=ifield, qfield=qfield))
self.fe.add(ProbalitySum(field=ifield, qfield=qfield))
self.fe.add(DfrGl2Stat(SumPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrGl2Stat(AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrGl2Stat(MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrGl2Stat(MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrGl2Stat(MinPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrGl2Stat(MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrInExpB2Stat(SumPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrInExpB2Stat(AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrInExpB2Stat(MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrInExpB2Stat(MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrInExpB2Stat(MinPooler(), field=ifield, qfield=qfield))
self.fe.add(DfrInExpB2Stat(MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(DphStat(SumPooler(), field=ifield, qfield=qfield))
self.fe.add(DphStat(AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(DphStat(MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(DphStat(MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(DphStat(MinPooler(), field=ifield, qfield=qfield))
self.fe.add(DphStat(MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(Proximity(field=ifield, qfield=qfield))
self.fe.add(TpScore(field=ifield, qfield=qfield))
self.fe.add(TpDist(field=ifield, qfield=qfield))
self.fe.add(DocSize(field=ifield))
self.fe.add(QueryLength(qfield=qfield))
self.fe.add(QueryCoverageRatio(qfield=qfield))
self.fe.add(UniqueTermCount(qfield=qfield))
self.fe.add(MatchingTermCount(field=ifield, qfield=qfield))
self.fe.add(SCS(field=ifield, qfield=qfield))
self.fe.add(TfStat(AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(TfStat(MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(TfStat(SumPooler(), field=ifield, qfield=qfield))
self.fe.add(TfStat(MinPooler(), field=ifield, qfield=qfield))
self.fe.add(TfStat(MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(TfStat(MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(TfIdfStat(True, AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(TfIdfStat(True, MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(TfIdfStat(True, SumPooler(), field=ifield, qfield=qfield))
self.fe.add(TfIdfStat(True, MinPooler(), field=ifield, qfield=qfield))
self.fe.add(TfIdfStat(True, MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(TfIdfStat(True, MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(NormalizedTfStat(AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(NormalizedTfStat(MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(NormalizedTfStat(SumPooler(), field=ifield, qfield=qfield))
self.fe.add(NormalizedTfStat(MinPooler(), field=ifield, qfield=qfield))
self.fe.add(NormalizedTfStat(MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(NormalizedTfStat(MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(IdfStat(AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(IdfStat(MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(IdfStat(SumPooler(), field=ifield, qfield=qfield))
self.fe.add(IdfStat(MinPooler(), field=ifield, qfield=qfield))
self.fe.add(IdfStat(MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(IdfStat(MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(IcTfStat(AvgPooler(), field=ifield, qfield=qfield))
self.fe.add(IcTfStat(MedianPooler(), field=ifield, qfield=qfield))
self.fe.add(IcTfStat(SumPooler(), field=ifield, qfield=qfield))
self.fe.add(IcTfStat(MinPooler(), field=ifield, qfield=qfield))
self.fe.add(IcTfStat(MaxPooler(), field=ifield, qfield=qfield))
self.fe.add(IcTfStat(MaxMinRatioPooler(), field=ifield, qfield=qfield))
self.fe.add(UnorderedSequentialPairs(3, field=ifield, qfield=qfield))
self.fe.add(UnorderedSequentialPairs(8, field=ifield, qfield=qfield))
self.fe.add(UnorderedSequentialPairs(15, field=ifield, qfield=qfield))
self.fe.add(OrderedSequentialPairs(3, field=ifield, qfield=qfield))
self.fe.add(OrderedSequentialPairs(8, field=ifield, qfield=qfield))
self.fe.add(OrderedSequentialPairs(15, field=ifield, qfield=qfield))
self.fe.add(UnorderedQueryPairs(3, field=ifield, qfield=qfield))
self.fe.add(UnorderedQueryPairs(8, field=ifield, qfield=qfield))
self.fe.add(UnorderedQueryPairs(15, field=ifield, qfield=qfield))
self.fe.add(OrderedQueryPairs(3, field=ifield, qfield=qfield))
self.fe.add(OrderedQueryPairs(8, field=ifield, qfield=qfield))
self.fe.add(OrderedQueryPairs(15, field=ifield, qfield=qfield))
start = time.time()
self.fe.add(IbmModel1(f"{self.ibm_model}/title_unlemm", "text_unlemm", "title_unlemm", "text_unlemm"))
end = time.time()
print('IBM model Load takes %.2f seconds' % (end - start))
start = end
self.fe.add(IbmModel1(f"{self.ibm_model}url_unlemm", "text_unlemm", "url_unlemm", "text_unlemm"))
end = time.time()
print('IBM model Load takes %.2f seconds' % (end - start))
start = end
self.fe.add(IbmModel1(f"{self.ibm_model}body", "text_unlemm", "body", "text_unlemm"))
end = time.time()
print('IBM model Load takes %.2f seconds' % (end - start))
start = end
self.fe.add(IbmModel1(f"{self.ibm_model}text_bert_tok", "text_bert_tok", "text_bert_tok", "text_bert_tok"))
end = time.time()
print('IBM model Load takes %.2f seconds' % (end - start))
start = end
def batch_extract(self, df, queries, fe):
tasks = []
task_infos = []
group_lst = []
for qid, group in tqdm(df.groupby('qid')):
task = {
"qid": qid,
"docIds": [],
"rels": [],
"query_dict": queries[qid]
}
for t in group.reset_index().itertuples():
if self.data == 'document':
if self.index_reader.doc(t.pid) != None:
task["docIds"].append(t.pid)
task_infos.append((qid, t.pid, t.rel))
else:
task["docIds"].append(t.pid)
task_infos.append((qid, t.pid, t.rel))
tasks.append(task)
group_lst.append((qid, len(task['docIds'])))
if len(tasks) == 1000:
features = fe.batch_extract(tasks)
task_infos = pd.DataFrame(task_infos, columns=['qid', 'pid', 'rel'])
group = pd.DataFrame(group_lst, columns=['qid', 'count'])
print(features.shape)
print(task_infos.qid.drop_duplicates().shape)
print(group.mean())
print(features.head(10))
print(features.info())
yield task_infos, features, group
tasks = []
task_infos = []
group_lst = []
# deal with rest
if len(tasks) > 0:
features = fe.batch_extract(tasks)
task_infos = pd.DataFrame(task_infos, columns=['qid', 'pid', 'rel'])
group = pd.DataFrame(group_lst, columns=['qid', 'count'])
print(features.shape)
print(task_infos.qid.drop_duplicates().shape)
print(group.mean())
print(features.head(10))
print(features.info())
yield task_infos, features, group
return
def batch_predict(self, models, dev_extracted, feature_name):
task_infos, features, group = dev_extracted
dev_X = features.loc[:, feature_name]
task_infos['score'] = 0.
for gbm in models:
task_infos['score'] += gbm.predict(dev_X)
def search(self, dev, queries):
batch_info = []
start_extract = time.time()
models = pickle.load(open(self.model+'/model.pkl', 'rb'))
metadata = json.load(open(self.model+'/metadata.json', 'r'))
feature_used = metadata['feature_names']
for dev_extracted in self.batch_extract(dev, queries, self.fe):
end_extract = time.time()
print(f'extract 1000 queries take {end_extract - start_extract}s')
task_infos, features, group = dev_extracted
start_predict = time.time()
self.batch_predict(models, dev_extracted, feature_used)
end_predict = time.time()
print(f'predict 1000 queries take {end_predict - start_predict}s')
batch_info.append(task_infos)
start_extract = time.time()
batch_info = pd.concat(batch_info, axis=0, ignore_index=True)
return batch_info
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