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import json
import logging
import os
from collections import defaultdict
from pathlib import Path
from time import time
from typing import Dict, Tuple
from datasets import Features, Value, load_dataset
from ..evaluation.evaluators import RetrievalEvaluator
from ..MTEBResults import ScoresDict
from .AbsTask import AbsTask
logger = logging.getLogger(__name__)
# Adapted from https://github.com/beir-cellar/beir/blob/f062f038c4bfd19a8ca942a9910b1e0d218759d4/beir/datasets/data_loader_hf.py#L10
class HFDataLoader:
def __init__(
self,
hf_repo: str | None = None,
hf_repo_qrels: str | None = None,
data_folder: str | None = None,
prefix: str | None = None,
corpus_file: str = "corpus.jsonl",
query_file: str = "queries.jsonl",
qrels_folder: str = "qrels",
qrels_file: str = "",
streaming: bool = False,
keep_in_memory: bool = False,
):
self.corpus = {}
self.queries = {}
self.qrels = {}
self.hf_repo = hf_repo
if hf_repo:
# By default fetch qrels from same repo not a second repo with "-qrels" like in original
self.hf_repo_qrels = hf_repo_qrels if hf_repo_qrels else hf_repo
else:
# data folder would contain these files:
# (1) fiqa/corpus.jsonl (format: jsonlines)
# (2) fiqa/queries.jsonl (format: jsonlines)
# (3) fiqa/qrels/test.tsv (format: tsv ("\t"))
if prefix:
query_file = prefix + "-" + query_file
qrels_folder = prefix + "-" + qrels_folder
self.corpus_file = (
os.path.join(data_folder, corpus_file) if data_folder else corpus_file
)
self.query_file = (
os.path.join(data_folder, query_file) if data_folder else query_file
)
self.qrels_folder = (
os.path.join(data_folder, qrels_folder) if data_folder else None
)
self.qrels_file = qrels_file
self.streaming = streaming
self.keep_in_memory = keep_in_memory
@staticmethod
def check(fIn: str, ext: str):
if not os.path.exists(fIn):
raise ValueError(
"File {} not present! Please provide accurate file.".format(fIn)
)
if not fIn.endswith(ext):
raise ValueError(
"File {} must be present with extension {}".format(fIn, ext)
)
def load(
self, split="test"
) -> Tuple[Dict[str, dict[str, str]], dict[str, str], dict[str, dict[str, int]]]:
if not self.hf_repo:
self.qrels_file = os.path.join(self.qrels_folder, split + ".tsv")
self.check(fIn=self.corpus_file, ext="jsonl")
self.check(fIn=self.query_file, ext="jsonl")
self.check(fIn=self.qrels_file, ext="tsv")
if not len(self.corpus):
logger.info("Loading Corpus...")
self._load_corpus()
logger.info("Loaded %d %s Documents.", len(self.corpus), split.upper())
logger.info("Doc Example: %s", self.corpus[0])
if not len(self.queries):
logger.info("Loading Queries...")
self._load_queries()
self._load_qrels(split)
# filter queries with no qrels
qrels_dict = defaultdict(dict)
def qrels_dict_init(row):
qrels_dict[row["query-id"]][row["corpus-id"]] = int(row["score"])
self.qrels.map(qrels_dict_init)
self.qrels = qrels_dict
self.queries = self.queries.filter(lambda x: x["id"] in self.qrels)
logger.info("Loaded %d %s Queries.", len(self.queries), split.upper())
logger.info("Query Example: %s", self.queries[0])
return self.corpus, self.queries, self.qrels
def load_corpus(self) -> dict[str, dict[str, str]]:
if not self.hf_repo:
self.check(fIn=self.corpus_file, ext="jsonl")
if not len(self.corpus):
logger.info("Loading Corpus...")
self._load_corpus()
logger.info("Loaded %d %s Documents.", len(self.corpus))
logger.info("Doc Example: %s", self.corpus[0])
return self.corpus
def _load_corpus(self):
if self.hf_repo:
corpus_ds = load_dataset(
self.hf_repo,
"corpus",
keep_in_memory=self.keep_in_memory,
streaming=self.streaming,
)
else:
corpus_ds = load_dataset(
"json",
data_files=self.corpus_file,
streaming=self.streaming,
keep_in_memory=self.keep_in_memory,
)
corpus_ds = next(iter(corpus_ds.values())) # get first split
corpus_ds = corpus_ds.cast_column("_id", Value("string"))
corpus_ds = corpus_ds.rename_column("_id", "id")
corpus_ds = corpus_ds.remove_columns(
[
col
for col in corpus_ds.column_names
if col not in ["id", "text", "title"]
]
)
self.corpus = corpus_ds
def _load_queries(self):
if self.hf_repo:
queries_ds = load_dataset(
self.hf_repo,
"queries",
keep_in_memory=self.keep_in_memory,
streaming=self.streaming,
)
else:
queries_ds = load_dataset(
"json",
data_files=self.query_file,
streaming=self.streaming,
keep_in_memory=self.keep_in_memory,
)
queries_ds = next(iter(queries_ds.values())) # get first split
queries_ds = queries_ds.cast_column("_id", Value("string"))
queries_ds = queries_ds.rename_column("_id", "id")
queries_ds = queries_ds.remove_columns(
[col for col in queries_ds.column_names if col not in ["id", "text"]]
)
self.queries = queries_ds
def _load_qrels(self, split):
if self.hf_repo:
qrels_ds = load_dataset(
self.hf_repo_qrels,
keep_in_memory=self.keep_in_memory,
streaming=self.streaming,
)[split]
else:
qrels_ds = load_dataset(
"csv",
data_files=self.qrels_file,
delimiter="\t",
keep_in_memory=self.keep_in_memory,
)
features = Features(
{
"query-id": Value("string"),
"corpus-id": Value("string"),
"score": Value("float"),
}
)
qrels_ds = qrels_ds.cast(features)
self.qrels = qrels_ds
class AbsTaskRetrieval(AbsTask):
"""Abstract class for re-ranking experiments.
Child-classes must implement the following properties:
self.corpus: dict[str, dict[str, str]]
Semantically, it should contain dict[split_name, dict[sample_id, dict[str, str]]]
E.g. {"test": {"document_one": {"_id": "d1", "title": "title", "text": "text"}}}
self.queries: dict[str, dict[str, Union[str, List[str]]]]
Semantically, it should contain dict[split_name, dict[sample_id, str]] or dict[split_name, dict[sample_id, List[str]]] for conversations
E.g. {"test": {"q1": "query"}}
or {"test": {"q1": ["turn1", "turn2", "turn3"]}}
self.relevant_docs: dict[str, dict[str, dict[str, int]]]
Semantically, it should contain dict[split_name, dict[sample_id, dict[doc_id, score]]]
E.g.: {"test": {"q1": {"document_one": 1}}}
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
def load_data(self, **kwargs):
if self.data_loaded:
return
self.corpus, self.queries, self.relevant_docs = {}, {}, {}
dataset_path = self.metadata_dict["dataset"]["path"]
hf_repo_qrels = (
dataset_path + "-qrels" if "clarin-knext" in dataset_path else None
)
for split in kwargs.get("eval_splits", self.metadata_dict["eval_splits"]):
corpus, queries, qrels = HFDataLoader(
hf_repo=dataset_path,
hf_repo_qrels=hf_repo_qrels,
streaming=False,
keep_in_memory=False,
).load(split=split)
# Conversion from DataSet
queries = {query["id"]: query["text"] for query in queries}
corpus = {
doc["id"]: {"title": doc["title"], "text": doc["text"]}
for doc in corpus
}
self.corpus[split], self.queries[split], self.relevant_docs[split] = (
corpus,
queries,
qrels,
)
self.data_loaded = True
def evaluate(self, model, split="test", **kwargs):
retriever = RetrievalEvaluator(model, **kwargs)
scores = {}
hf_subsets = (
[l for l in self.hf_subsets]
if (self.is_multilingual or self.is_crosslingual)
else ["default"]
)
for hf_subset in hf_subsets:
logger.info(f"Subset: {hf_subset}")
if hf_subset == "default":
corpus, queries, relevant_docs = (
self.corpus[split],
self.queries[split],
self.relevant_docs[split],
)
else:
corpus, queries, relevant_docs = (
self.corpus[hf_subset][split],
self.queries[hf_subset][split],
self.relevant_docs[hf_subset][split],
)
scores[hf_subset] = self._evaluate_subset(
retriever, corpus, queries, relevant_docs, hf_subset, **kwargs
)
return scores
def _evaluate_subset(
self, retriever, corpus, queries, relevant_docs, hf_subset: str, **kwargs
):
start_time = time()
results = retriever(corpus, queries)
end_time = time()
logger.info(
"Time taken to retrieve: {:.2f} seconds".format(end_time - start_time)
)
if kwargs.get("save_predictions", False):
output_folder = Path(kwargs.get("output_folder", "results"))
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
top_k = kwargs.get("top_k", None)
if top_k is not None:
for qid in list(results.keys()):
doc_ids = set(
sorted(
results[qid], key=lambda x: results[qid][x], reverse=True
)[:top_k]
)
results[qid] = {
k: v for k, v in results[qid].items() if k in doc_ids
}
qrels_save_path = (
output_folder
/ f"{self.metadata_dict['name']}_{hf_subset}_predictions.json"
)
with open(qrels_save_path, "w") as f:
json.dump(results, f)
ndcg, _map, recall, precision = retriever.evaluate(
relevant_docs,
results,
retriever.k_values,
ignore_identical_ids=kwargs.get("ignore_identical_ids", True),
)
mrr = retriever.evaluate_custom(
relevant_docs, results, retriever.k_values, "mrr"
)
scores = {
**{f"ndcg_at_{k.split('@')[1]}": v for (k, v) in ndcg.items()},
**{f"map_at_{k.split('@')[1]}": v for (k, v) in _map.items()},
**{f"recall_at_{k.split('@')[1]}": v for (k, v) in recall.items()},
**{f"precision_at_{k.split('@')[1]}": v for (k, v) in precision.items()},
**{f"mrr_at_{k.split('@')[1]}": v for (k, v) in mrr.items()},
}
self._add_main_score(scores)
return scores
def _add_main_score(self, scores: ScoresDict) -> None:
scores["main_score"] = scores[self.metadata.main_score]
def calculate_metadata_metrics(self) -> None:
self.load_data()
for split in self.metadata_dict["eval_splits"]:
if self.is_multilingual:
for lang in self.relevant_docs.keys():
process_language(
self.relevant_docs[lang][split],
self.queries[lang][split],
self.corpus[lang][split],
lang,
)
else:
process_language(
self.relevant_docs[split], self.queries[split], self.corpus[split]
)
def process_language(relevant_docs, queries, corpus, lang=None):
total_length, num_pairs = calculate_length_and_count(relevant_docs, queries, corpus)
average_length = total_length / num_pairs if num_pairs else 0
num_documents = len(queries) + len(corpus)
language_description = f" for language {lang}" if lang else ""
print(f"Average character length{language_description} is {average_length}")
print(f"Number of queries and documents{language_description} is {num_documents}")
def calculate_length_and_count(relevant_docs, queries, corpus):
total_length = 0
num_pairs = 0
for query_id, docs in relevant_docs.items():
query = queries[query_id]
for doc_id in docs:
# not relevant
if docs[doc_id] == 0:
continue
doc = corpus[doc_id]
doc_text = doc["title"] + doc["text"]
total_length += len(query) + len(doc_text)
num_pairs += 1
return total_length, num_pairs
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