refactor: Rename DocumentMeanAveragePrecision and `DocumentMeanReci…

…procalRank` (#7470)

* Rename DocumentMeanAveragePrecision and DocumentMeanReciprocalRank

* Update releasenotes

* Simplify names

haystack/components/evaluators/document_map.py

@@ -4,20 +4,20 @@
 
 
 @component
-class DocumentMeanAveragePrecision:
+class DocumentMAPEvaluator:
     """
     Evaluator that calculates the mean average precision of the retrieved documents, a metric
     that measures how high retrieved documents are ranked.
     Each question can have multiple ground truth documents and multiple retrieved documents.
 
-    `DocumentMeanAveragePrecision` doesn't normalize its inputs, the `DocumentCleaner` component
+    `DocumentMAPEvaluator` doesn't normalize its inputs, the `DocumentCleaner` component
     should be used to clean and normalize the documents before passing them to this evaluator.
 
     Usage example:
     ```python
     from haystack.components.evaluators import AnswerExactMatchEvaluator
 
-    evaluator = DocumentMeanAveragePrecision()
+    evaluator = DocumentMAPEvaluator()
     result = evaluator.run(
         ground_truth_documents=[
             [Document(content="France")],
@@ -41,7 +41,7 @@ def run(
         self, ground_truth_documents: List[List[Document]], retrieved_documents: List[List[Document]]
     ) -> Dict[str, Any]:
         """
-        Run the DocumentMeanAveragePrecision on the given inputs.
+        Run the DocumentMAPEvaluator on the given inputs.
         All lists must have the same length.
 
         :param ground_truth_documents:

haystack/components/evaluators/document_mrr.py

@@ -4,20 +4,20 @@
 
 
 @component
-class DocumentMeanReciprocalRank:
+class DocumentMRREvaluator:
     """
     Evaluator that calculates the mean reciprocal rank of the retrieved documents.
 
     MRR measures how high the first retrieved document is ranked.
     Each question can have multiple ground truth documents and multiple retrieved documents.
 
-    `DocumentMeanReciprocalRank` doesn't normalize its inputs, the `DocumentCleaner` component
+    `DocumentMRREvaluator` doesn't normalize its inputs, the `DocumentCleaner` component
     should be used to clean and normalize the documents before passing them to this evaluator.
 
     Usage example:
     ```python
     from haystack.components.evaluators import AnswerExactMatchEvaluator
-    evaluator = DocumentMeanReciprocalRank()
+    evaluator = DocumentMRREvaluator()
     result = evaluator.run(
         ground_truth_documents=[
             [Document(content="France")],
@@ -40,7 +40,7 @@ def run(
         self, ground_truth_documents: List[List[Document]], retrieved_documents: List[List[Document]]
     ) -> Dict[str, Any]:
         """
-        Run the DocumentMeanReciprocalRank on the given inputs.
+        Run the DocumentMRREvaluator on the given inputs.
 
         `ground_truth_documents` and `retrieved_documents` must have the same length.
 

releasenotes/notes/document-map-evaluator-de896c94b54fe3fa.yaml

@@ -1,4 +1,4 @@
 ---
 features:
   - |
-    Add DocumentMeanAveragePrecision, it can be used to calculate mean average precision of retrieved documents.
+    Add DocumentMAPEvaluator, it can be used to calculate mean average precision of retrieved documents.

releasenotes/notes/document-mrr-evaluator-fa7c266cc91201a7.yaml

@@ -1,4 +1,4 @@
 ---
 features:
   - |
-    Add DocumentMeanReciprocalRank, it can be used to calculate mean reciprocal rank of retrieved documents.
+    Add DocumentMRREvaluator, it can be used to calculate mean reciprocal rank of retrieved documents.

test/components/evaluators/test_document_map.py

@@ -1,11 +1,11 @@
 import pytest
 
 from haystack import Document
-from haystack.components.evaluators.document_map import DocumentMeanAveragePrecision
+from haystack.components.evaluators.document_map import DocumentMAPEvaluator
 
 
 def test_run_with_all_matching():
-    evaluator = DocumentMeanAveragePrecision()
+    evaluator = DocumentMAPEvaluator()
     result = evaluator.run(
         ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
         retrieved_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
@@ -15,7 +15,7 @@ def test_run_with_all_matching():
 
 
 def test_run_with_no_matching():
-    evaluator = DocumentMeanAveragePrecision()
+    evaluator = DocumentMAPEvaluator()
     result = evaluator.run(
         ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
         retrieved_documents=[[Document(content="Paris")], [Document(content="London")]],
@@ -25,7 +25,7 @@ def test_run_with_no_matching():
 
 
 def test_run_with_partial_matching():
-    evaluator = DocumentMeanAveragePrecision()
+    evaluator = DocumentMAPEvaluator()
     result = evaluator.run(
         ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
         retrieved_documents=[[Document(content="Berlin")], [Document(content="London")]],
@@ -35,7 +35,7 @@ def test_run_with_partial_matching():
 
 
 def test_run_with_complex_data():
-    evaluator = DocumentMeanAveragePrecision()
+    evaluator = DocumentMAPEvaluator()
     result = evaluator.run(
         ground_truth_documents=[
             [Document(content="France")],
@@ -64,14 +64,14 @@ def test_run_with_complex_data():
 
 def test_run_with_different_lengths():
     with pytest.raises(ValueError):
-        evaluator = DocumentMeanAveragePrecision()
+        evaluator = DocumentMAPEvaluator()
         evaluator.run(
             ground_truth_documents=[[Document(content="Berlin")]],
             retrieved_documents=[[Document(content="Berlin")], [Document(content="London")]],
         )
 
     with pytest.raises(ValueError):
-        evaluator = DocumentMeanAveragePrecision()
+        evaluator = DocumentMAPEvaluator()
         evaluator.run(
             ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
             retrieved_documents=[[Document(content="Berlin")]],

test/components/evaluators/test_document_mrr.py

@@ -1,11 +1,11 @@
 import pytest
 
 from haystack import Document
-from haystack.components.evaluators.document_mrr import DocumentMeanReciprocalRank
+from haystack.components.evaluators.document_mrr import DocumentMRREvaluator
 
 
 def test_run_with_all_matching():
-    evaluator = DocumentMeanReciprocalRank()
+    evaluator = DocumentMRREvaluator()
     result = evaluator.run(
         ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
         retrieved_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
@@ -15,7 +15,7 @@ def test_run_with_all_matching():
 
 
 def test_run_with_no_matching():
-    evaluator = DocumentMeanReciprocalRank()
+    evaluator = DocumentMRREvaluator()
     result = evaluator.run(
         ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
         retrieved_documents=[[Document(content="Paris")], [Document(content="London")]],
@@ -25,7 +25,7 @@ def test_run_with_no_matching():
 
 
 def test_run_with_partial_matching():
-    evaluator = DocumentMeanReciprocalRank()
+    evaluator = DocumentMRREvaluator()
     result = evaluator.run(
         ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
         retrieved_documents=[[Document(content="Berlin")], [Document(content="London")]],
@@ -35,7 +35,7 @@ def test_run_with_partial_matching():
 
 
 def test_run_with_complex_data():
-    evaluator = DocumentMeanReciprocalRank()
+    evaluator = DocumentMRREvaluator()
     result = evaluator.run(
         ground_truth_documents=[
             [Document(content="France")],
@@ -68,14 +68,14 @@ def test_run_with_complex_data():
 
 def test_run_with_different_lengths():
     with pytest.raises(ValueError):
-        evaluator = DocumentMeanReciprocalRank()
+        evaluator = DocumentMRREvaluator()
         evaluator.run(
             ground_truth_documents=[[Document(content="Berlin")]],
             retrieved_documents=[[Document(content="Berlin")], [Document(content="London")]],
         )
 
     with pytest.raises(ValueError):
-        evaluator = DocumentMeanReciprocalRank()
+        evaluator = DocumentMRREvaluator()
         evaluator.run(
             ground_truth_documents=[[Document(content="Berlin")], [Document(content="Paris")]],
             retrieved_documents=[[Document(content="Berlin")]],