testbed/embeddings-benchmark__mteb/mteb/abstasks/AbsTaskRetrieval.py

from __future__ import annotations

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