add metric for evaluation: evaluate_recall_cap

FlagEmbedding/abc/evaluation/evaluator.py

@@ -10,7 +10,7 @@
 
 from .data_loader import AbsEvalDataLoader
 from .searcher import EvalRetriever, EvalReranker
-from .utils import evaluate_metrics, evaluate_mrr
+from .utils import evaluate_metrics, evaluate_mrr, evaluate_recall_cap
 
 logger = logging.getLogger(__name__)
 
@@ -340,12 +340,18 @@ def compute_metrics(
             results=search_results,
             k_values=k_values,
         )
+        recall_cap = evaluate_recall_cap(
+            qrels=qrels,
+            results=search_results,
+            k_values=k_values,
+        )
         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()},
+            **{f"recall_cap_at_{k.split('@')[1]}": v for (k, v) in recall_cap.items()},
         }
         return scores
 

FlagEmbedding/abc/evaluation/utils.py

@@ -52,6 +52,45 @@ def evaluate_mrr(
     return mrr
 
 
+# Modified from https://github.com/beir-cellar/beir/blob/f062f038c4bfd19a8ca942a9910b1e0d218759d4/beir/retrieval/custom_metrics.py#L33
+def evaluate_recall_cap(
+    qrels: Dict[str, Dict[str, int]], 
+    results: Dict[str, Dict[str, float]], 
+    k_values: List[int]
+) -> Tuple[Dict[str, float]]:
+    """Compute capped recall.
+
+    Args:
+        qrels (Dict[str, Dict[str, int]]): Ground truth relevance.
+        results (Dict[str, Dict[str, float]]): Search results to evaluate.
+        k_values (List[int]): Cutoffs.
+
+    Returns:
+        Tuple[Dict[str, float]]: Capped recall results at provided k values.
+    """
+    capped_recall = {}
+
+    for k in k_values:
+        capped_recall[f"R_cap@{k}"] = 0.0
+
+    k_max = max(k_values)
+    logging.info("\n")
+
+    for query_id, doc_scores in results.items():
+        top_hits = sorted(doc_scores.items(), key=lambda item: item[1], reverse=True)[0:k_max]   
+        query_relevant_docs = [doc_id for doc_id in qrels[query_id] if qrels[query_id][doc_id] > 0]
+        for k in k_values:
+            retrieved_docs = [row[0] for row in top_hits[0:k] if qrels[query_id].get(row[0], 0) > 0]
+            denominator = min(len(query_relevant_docs), k)
+            capped_recall[f"R_cap@{k}"] += (len(retrieved_docs) / denominator)
+
+    for k in k_values:
+        capped_recall[f"R_cap@{k}"] = round(capped_recall[f"R_cap@{k}"]/len(qrels), 5)
+        logging.info("R_cap@{}: {:.4f}".format(k, capped_recall[f"R_cap@{k}"]))
+
+    return capped_recall
+
+
 # Modified from https://github.com/embeddings-benchmark/mteb/blob/18f730696451a5aaa026494cecf288fd5cde9fd0/mteb/evaluation/evaluators/RetrievalEvaluator.py#L501
 def evaluate_metrics(
     qrels: Dict[str, Dict[str, int]],