cde_benchmark/evaluators/eval_utils.py

# from mteb.evaluation.evaluators.RetrievalEvaluator
from __future__ import annotations

import logging
from typing import Dict

import numpy as np
import pytrec_eval
from mteb.evaluation.evaluators.RetrievalEvaluator import RetrievalEvaluator
from mteb.evaluation.evaluators.utils import (
    confidence_scores,
    hole,
    mrr,
    nAUC,
    recall_cap,
    top_k_accuracy,
)

logger = logging.getLogger(__name__)


class CustomRetrievalEvaluator:
    """
    Wrapper class for the MTEB retrieval evaluator.
    """

    def __init__(self, k_values: list[int] = [1, 3, 5, 10, 20, 50, 100]):
        self.k_values = k_values

    def compute_mteb_metrics(
        self,
        relevant_docs: Dict[str, dict[str, int]],
        results: Dict[str, dict[str, float]],
        **kwargs,
    ) -> Dict[str, float]:
        """
        Compute the MTEB retrieval metrics.
        """
        ndcg, _map, recall, precision, naucs = self.evaluate(
            relevant_docs,
            results,
            self.k_values,
            ignore_identical_ids=kwargs.get("ignore_identical_ids", True),
        )

        mrr = self.evaluate_custom(relevant_docs, results, self.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[0].items()},
            **{f"naucs_at_{k.split('@')[1]}": v for (k, v) in naucs.items()},
        }
        return scores

    @staticmethod
    def evaluate(
        qrels: dict[str, dict[str, int]],
        results: dict[str, dict[str, float]],
        k_values: list[int],
        ignore_identical_ids: bool = False,
    ) -> tuple[
        dict[str, float],
        dict[str, float],
        dict[str, float],
        dict[str, float],
        dict[str, float],
    ]:
        if ignore_identical_ids:
            logger.debug(
                "For evaluation, ``ignore_identical_ids=True`` is set to True, the evaluator will ignore "
                "identical query and document ids."
            )
            # Remove identical ids from results dict
            for qid, rels in results.items():
                for pid in list(rels):
                    if qid == pid:
                        results[qid].pop(pid)
        else:
            logger.debug(
                "For evaluation, we DO NOT ignore identical query and document ids (default), please explicitly "
                "set ``ignore_identical_ids=True`` to ignore this."
            )

        all_ndcgs, all_aps, all_recalls, all_precisions = {}, {}, {}, {}

        for k in k_values:
            all_ndcgs[f"NDCG@{k}"] = []
            all_aps[f"MAP@{k}"] = []
            all_recalls[f"Recall@{k}"] = []
            all_precisions[f"P@{k}"] = []

        map_string = "map_cut." + ",".join([str(k) for k in k_values])
        ndcg_string = "ndcg_cut." + ",".join([str(k) for k in k_values])
        recall_string = "recall." + ",".join([str(k) for k in k_values])
        precision_string = "P." + ",".join([str(k) for k in k_values])
        evaluator = pytrec_eval.RelevanceEvaluator(
            qrels, {map_string, ndcg_string, recall_string, precision_string}
        )
        scores = evaluator.evaluate(results)

        for query_id in scores.keys():
            for k in k_values:
                all_ndcgs[f"NDCG@{k}"].append(scores[query_id]["ndcg_cut_" + str(k)])
                all_aps[f"MAP@{k}"].append(scores[query_id]["map_cut_" + str(k)])
                all_recalls[f"Recall@{k}"].append(scores[query_id]["recall_" + str(k)])
                all_precisions[f"P@{k}"].append(scores[query_id]["P_" + str(k)])

        ndcg, _map, recall, precision = (
            all_ndcgs.copy(),
            all_aps.copy(),
            all_recalls.copy(),
            all_precisions.copy(),
        )

        for k in k_values:
            ndcg[f"NDCG@{k}"] = round(sum(ndcg[f"NDCG@{k}"]) / len(scores), 5)
            _map[f"MAP@{k}"] = round(sum(_map[f"MAP@{k}"]) / len(scores), 5)
            recall[f"Recall@{k}"] = round(sum(recall[f"Recall@{k}"]) / len(scores), 5)
            precision[f"P@{k}"] = round(sum(precision[f"P@{k}"]) / len(scores), 5)

        naucs = RetrievalEvaluator.evaluate_abstention(
            results, {**all_ndcgs, **all_aps, **all_recalls, **all_precisions}
        )

        return ndcg, _map, recall, precision, naucs

    @staticmethod
    def evaluate_custom(
        qrels: dict[str, dict[str, int]],
        results: dict[str, dict[str, float]],
        k_values: list[int],
        metric: str,
        output_type: str = "all",
    ) -> tuple[dict[str, float], dict[str, float]]:
        if metric.lower() in ["mrr", "mrr@k", "mrr_cut"]:
            metric_scores = mrr(qrels, results, k_values, output_type)

        elif metric.lower() in ["recall_cap", "r_cap", "r_cap@k"]:
            metric_scores = recall_cap(qrels, results, k_values, output_type)

        elif metric.lower() in ["hole", "hole@k"]:
            metric_scores = hole(qrels, results, k_values, output_type)

        elif metric.lower() in [
            "acc",
            "top_k_acc",
            "accuracy",
            "accuracy@k",
            "top_k_accuracy",
        ]:
            metric_scores = top_k_accuracy(qrels, results, k_values, output_type)

        naucs = RetrievalEvaluator.evaluate_abstention(results, metric_scores)
        metric_scores_avg = {k: sum(v) / len(v) for k, v in metric_scores.items()}

        return metric_scores_avg, naucs

    @staticmethod
    def evaluate_abstention(
        results: dict[str, dict[str, float]],
        metric_scores: dict[str, list[float]],
    ) -> dict[str, float]:
        """Computes normalized Area Under the Curve on a set of evaluated instances as presented in
        the paper https://arxiv.org/abs/2402.12997"""
        all_sim_scores = [list(results[qid].values()) for qid in list(results.keys())]
        all_conf_scores = [
            confidence_scores(sim_scores) for sim_scores in all_sim_scores
        ]
        conf_fcts = list(all_conf_scores[0].keys())
        all_conf_scores = {
            fct: np.array([x[fct] for x in all_conf_scores]) for fct in conf_fcts
        }
        metric_scores = {k: np.array(v) for k, v in metric_scores.items()}
        naucs = {}

        for metric_name, scores in metric_scores.items():
            for fct, conf_scores in all_conf_scores.items():
                naucs[f"nAUC_{metric_name}_{fct}"] = nAUC(conf_scores, scores)

        return naucs