def get_query_doc_scores(self, query, did, model, skip_invividual=False):
sim = self._model(model)
self._searcher().setSimilarity(sim)
ldid = self._get_index_utils().convertDocidToLuceneDocid(did)
if ldid == -1:
return -999. * len(query), [-999.] * len(query)
analyzer = self._get_stemmed_analyzer()
query = list(itertools.chain(*[J.A_AnalyzerUtils.analyze(analyzer, t).toArray() for t in query]))
if not skip_invividual:
result = []
for q in query:
q = _anserini_escape(q, J)
lquery = J.L_QueryParser().parse(q, J.A_IndexArgs.CONTENTS)
explain = self._searcher().explain(lquery, ldid)
result.append(explain.getValue().doubleValue())
return sum(result), result
lquery = J.L_QueryParser().parse(_anserini_escape(' '.join(query), J), J.A_IndexArgs.CONTENTS)
explain = self._searcher().explain(lquery, ldid)
return explain.getValue()
def get_query_doc_scores_batch(self, query, dids, model):
sim = self._model(model)
self._searcher().setSimilarity(sim)
ldids = {self._get_index_utils().convertDocidToLuceneDocid(did): did for did in dids}
analyzer = self._get_stemmed_analyzer()
query = J.A_AnalyzerUtils.analyze(analyzer, query).toArray()
query = ' '.join(_anserini_escape(q, J) for q in query)
docs = ' '.join(f'{J.A_IndexArgs.ID}:{did}' for did in dids)
lquery = J.L_QueryParser().parse(f'({query}) AND ({docs})', J.A_IndexArgs.CONTENTS)
result = {}
search_results = self._searcher().search(lquery, len(dids))
for top_doc in search_results.scoreDocs:
result[ldids[top_doc.doc]] = top_doc.score
del search_results
return result
