| from __future__ import annotations |
| import csv |
| import json |
| import os |
| import tempfile |
| import zipfile |
| from dataclasses import dataclass |
| from pathlib import Path |
|
|
|
|
| @dataclass |
| class BEIRDataset: |
| corpus_ids: list[str] |
| corpus_texts: list[str] |
| queries: dict[str, str] |
| qrels: dict[str, dict[str, float]] |
| name: str = "dataset" |
|
|
|
|
| def _find(root: Path, basename: str) -> Path: |
| hits = list(root.rglob(basename)) |
| if not hits: |
| raise FileNotFoundError(f"Could not find {basename!r} under {root}") |
| if len(hits) > 1: |
| |
| hits.sort(key=lambda p: len(p.parts)) |
| return hits[0] |
|
|
|
|
| def load_beir_directory(path: str | os.PathLike, split: str = "test") -> BEIRDataset: |
| root = Path(path) |
| corpus_path = _find(root, "corpus.jsonl") |
| queries_path = _find(root, "queries.jsonl") |
| qrels_hits = list(root.rglob(f"qrels/{split}.tsv")) |
| if not qrels_hits: |
| |
| qrels_hits = [p for p in root.rglob(f"{split}.tsv") if p.parent.name == "qrels"] |
| if not qrels_hits: |
| raise FileNotFoundError(f"Could not find qrels/{split}.tsv under {root}") |
| qrels_path = qrels_hits[0] |
|
|
| corpus_ids, corpus_texts = [], [] |
| with corpus_path.open("r", encoding="utf-8") as f: |
| for line in f: |
| if not line.strip(): |
| continue |
| obj = json.loads(line) |
| did = str(obj.get("_id", obj.get("id"))) |
| title = obj.get("title", "") or "" |
| text = obj.get("text", "") or "" |
| merged = (title + " " + text).strip() |
| corpus_ids.append(did) |
| corpus_texts.append(merged) |
|
|
| queries = {} |
| with queries_path.open("r", encoding="utf-8") as f: |
| for line in f: |
| if not line.strip(): |
| continue |
| obj = json.loads(line) |
| qid = str(obj.get("_id", obj.get("id"))) |
| queries[qid] = obj.get("text", "") or "" |
|
|
| qrels: dict[str, dict[str, float]] = {} |
| with qrels_path.open("r", encoding="utf-8") as f: |
| reader = csv.DictReader(f, delimiter="\t") |
| for row in reader: |
| |
| qid = str(row.get("query-id", row.get("query_id", row.get("qid")))) |
| did = str(row.get("corpus-id", row.get("corpus_id", row.get("docid")))) |
| score = float(row.get("score", row.get("relevance", row.get("rel", 0)))) |
| qrels.setdefault(qid, {})[did] = score |
|
|
| name = corpus_path.parent.name |
| return BEIRDataset(corpus_ids, corpus_texts, queries, qrels, name=name) |
|
|
|
|
| def load_beir_zip(path: str | os.PathLike, split: str = "test") -> BEIRDataset: |
| """Load a standard BEIR zip without requiring internet access.""" |
| with tempfile.TemporaryDirectory(prefix="geomretrieval_beir_") as td: |
| with zipfile.ZipFile(path, "r") as zf: |
| zf.extractall(td) |
| return load_beir_directory(td, split=split) |
|
|
