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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
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