evaluate_sparta.py

import logging
import os
import pathlib
import random

from beir import LoggingHandler, util
from beir.datasets.data_loader import GenericDataLoader
from beir.retrieval import models
from beir.retrieval.evaluation import EvaluateRetrieval
from beir.retrieval.search.sparse import SparseSearch

#### Just some code to print debug information to stdout
logging.basicConfig(
    format="%(asctime)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=logging.INFO,
    handlers=[LoggingHandler()],
)
#### /print debug information to stdout

dataset = "scifact"

#### Download scifact dataset and unzip the dataset
url = f"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{dataset}.zip"
out_dir = os.path.join(pathlib.Path(__file__).parent.absolute(), "datasets")
data_path = util.download_and_unzip(url, out_dir)

#### Provide the data path where scifact has been downloaded and unzipped to the data loader
# data folder would contain these files:
# (1) scifact/corpus.jsonl  (format: jsonlines)
# (2) scifact/queries.jsonl (format: jsonlines)
# (3) scifact/qrels/test.tsv (format: tsv ("\t"))

corpus, queries, qrels = GenericDataLoader(data_folder=data_path).load(split="test")

#### Sparse Retrieval using SPARTA ####
model_path = "BeIR/sparta-msmarco-distilbert-base-v1"
sparse_model = SparseSearch(models.SPARTA(model_path), batch_size=128)
retriever = EvaluateRetrieval(sparse_model)

#### Retrieve dense results (format of results is identical to qrels)
results = retriever.retrieve(corpus, queries)

#### Evaluate your retrieval using NDCG@k, MAP@K ...

logging.info(f"Retriever evaluation for k in: {retriever.k_values}")
ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)

#### Print top-k documents retrieved ####
top_k = 10

query_id, ranking_scores = random.choice(list(results.items()))
scores_sorted = sorted(ranking_scores.items(), key=lambda item: item[1], reverse=True)
logging.info(f"Query : {queries[query_id]}\n")

for rank in range(top_k):
    doc_id = scores_sorted[rank][0]
    # Format: Rank x: ID [Title] Body
    logging.info(f"Rank {rank + 1}: {doc_id} [{corpus[doc_id].get('title')}] - {corpus[doc_id].get('text')}\n")