Source code for

import json
import re

from capreolus import ConfigOption, Dependency, constants
from capreolus.utils.loginit import get_logger
from capreolus.utils.trec import topic_to_trectxt

from . import Benchmark

[docs]logger = get_logger(__name__)
[docs]PACKAGE_PATH = constants["PACKAGE_PATH"]
[docs]@Benchmark.register class NF(Benchmark): """NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval [1] [1] Vera Boteva, Demian Gholipour, Artem Sokolov and Stefan Riezler. A Full-Text Learning to Rank Dataset for Medical Information Retrieval Proceedings of the 38th European Conference on Information Retrieval (ECIR), Padova, Italy, 2016. """
[docs] module_name = "nf"
[docs] dependencies = [Dependency(key="collection", module="collection", name="nf")]
[docs] config_spec = [ ConfigOption(key="labelrange", default_value="0-2", description="range of dataset qrels, options: 0-2, 1-3"), ConfigOption( key="fields", default_value="all_titles", description="query fields included in topic file, "
"options: 'all_fields', 'all_titles', 'nontopics', 'vid_title', 'vid_desc'", ), ]
[docs] fold_file = PACKAGE_PATH / "data" / "nf.json"
[docs] query_type = "title"
[docs] def build(self): fields, label_range = self.config["fields"], self.config["labelrange"] self.field2kws = { "all_fields": ["all"], "nontopics": ["nontopic-titles"], "vid_title": ["vid-titles"], "vid_desc": ["vid-desc"], "all_titles": ["titles", "vid-titles", "nontopic-titles"], } self.labelrange2kw = {"0-2": "2-1-0", "1-3": "3-2-1"} if fields not in self.field2kws: raise ValueError(f"Unexpected fields value: {fields}, expect: {', '.join(self.field2kws.keys())}") if label_range not in self.labelrange2kw: raise ValueError(f"Unexpected label range: {label_range}, expect: {', '.join(self.field2kws.keys())}") self.qrel_file = PACKAGE_PATH / "data" / f"{label_range}.txt" self.test_qrel_file = PACKAGE_PATH / "data" / f"{label_range}.txt" self.topic_file = PACKAGE_PATH / "data" / f"{fields}.txt" self.download_if_missing()
def _transform_qid(self, raw): """ NFCorpus dataset specific, remove prefix in query id since anserini convert all qid to integer """ return raw.replace("PLAIN-", "")
[docs] def download_if_missing(self): if all([f.exists() for f in [self.topic_file, self.fold_file, self.qrel_file]]): return tmp_corpus_dir = self.collection.download_raw() topic_f = open(self.topic_file, "w", encoding="utf-8") qrel_f = open(self.qrel_file, "w", encoding="utf-8") test_qrel_f = open(self.test_qrel_file, "w", encoding="utf-8") set_names = ["train", "dev", "test"] folds = {s: set() for s in set_names} qrel_kw = self.labelrange2kw[self.config["labelrange"]] for set_name in set_names: with open(tmp_corpus_dir / f"{set_name}.{qrel_kw}.qrel") as f: for line in f: line = self._transform_qid(line) qid = line.strip().split()[0] folds[set_name].add(qid) if set_name == "test": test_qrel_f.write(line) qrel_f.write(line) files = [tmp_corpus_dir / f"{set_name}.{keyword}.queries" for keyword in self.field2kws[self.config["fields"]]] qids2topics = self._align_queries(files, "title") for qid, txts in qids2topics.items(): topic_f.write(topic_to_trectxt(qid, txts["title"])) json.dump( {"s1": {"train_qids": list(folds["train"]), "predict": {"dev": list(folds["dev"]), "test": list(folds["test"])}}}, open(self.fold_file, "w"), ) topic_f.close() qrel_f.close() test_qrel_f.close()"nf benchmark prepared")
def _align_queries(self, files, field, qid2queries=None): if not qid2queries: qid2queries = {} for fn in files: with open(fn, "r", encoding="utf-8") as f: for line in f: qid, txt = line.strip().split("\t") qid = self._transform_qid(qid) txt = " ".join(re.sub("[^A-Za-z]", " ", txt).split()[:1020]) if qid not in qid2queries: qid2queries[qid] = {field: txt} else: if field in qid2queries[qid]: logger.warning(f"Overwriting title for query {qid}") qid2queries[qid][field] = txt return qid2queries