fix: typo in sas_evaluator arg (#7486)

* fixing typo on SAS arg

* fixing tests

* fixing tests

haystack/components/evaluators/sas_evaluator.py

@@ -129,7 +129,7 @@ def warm_up(self):
             self._similarity_model = SentenceTransformer(self._model, device=device, use_auth_token=token)
 
     @component.output_types(score=float, individual_scores=List[float])
-    def run(self, ground_truths_answers: List[str], predicted_answers: List[str]) -> Dict[str, Any]:
+    def run(self, ground_truth_answers: List[str], predicted_answers: List[str]) -> Dict[str, Any]:
         """
         Run the SASEvaluator to compute the Semantic Answer Similarity (SAS) between a list of predicted answers
         and a list of ground truth answers. Both must be list of strings of same length.
@@ -143,7 +143,7 @@ def run(self, ground_truths_answers: List[str], predicted_answers: List[str]) ->
                 - `score`: Mean SAS score over all the predictions/ground-truth pairs.
                 - `individual_scores`: A list of similarity scores for each prediction/ground-truth pair.
         """
-        if len(ground_truths_answers) != len(predicted_answers):
+        if len(ground_truth_answers) != len(predicted_answers):
             raise ValueError("The number of predictions and labels must be the same.")
 
         if len(predicted_answers) == 0:
@@ -155,7 +155,7 @@ def run(self, ground_truths_answers: List[str], predicted_answers: List[str]) ->
 
         if isinstance(self._similarity_model, CrossEncoder):
             # For Cross Encoders we create a list of pairs of predictions and labels
-            sentence_pairs = list(zip(predicted_answers, ground_truths_answers))
+            sentence_pairs = list(zip(predicted_answers, ground_truth_answers))
             similarity_scores = self._similarity_model.predict(
                 sentence_pairs, batch_size=self._batch_size, convert_to_numpy=True
             )
@@ -174,7 +174,7 @@ def run(self, ground_truths_answers: List[str], predicted_answers: List[str]) ->
                 predicted_answers, batch_size=self._batch_size, convert_to_tensor=True
             )
             label_embeddings = self._similarity_model.encode(
-                ground_truths_answers, batch_size=self._batch_size, convert_to_tensor=True
+                ground_truth_answers, batch_size=self._batch_size, convert_to_tensor=True
             )
 
             # Compute cosine-similarities

test/components/evaluators/test_sas_evaluator.py

@@ -51,7 +51,7 @@ def test_from_dict(self, monkeypatch):
 
     def test_run_with_empty_inputs(self):
         evaluator = SASEvaluator()
-        result = evaluator.run(ground_truths_answers=[], predicted_answers=[])
+        result = evaluator.run(ground_truth_answers=[], predicted_answers=[])
         assert len(result) == 2
         assert result["score"] == 0.0
         assert result["individual_scores"] == [0.0]
@@ -68,7 +68,7 @@ def test_run_with_different_lengths(self):
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
         with pytest.raises(ValueError):
-            evaluator.run(ground_truths_answers=ground_truths, predicted_answers=predictions)
+            evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
 
     def test_run_not_warmed_up(self):
         evaluator = SASEvaluator()
@@ -83,7 +83,7 @@ def test_run_not_warmed_up(self):
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
         with pytest.raises(RuntimeError):
-            evaluator.run(ground_truths_answers=ground_truths, predicted_answers=predictions)
+            evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
 
     @pytest.mark.integration
     def test_run_with_matching_predictions(self):
@@ -99,7 +99,7 @@ def test_run_with_matching_predictions(self):
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
         evaluator.warm_up()
-        result = evaluator.run(ground_truths_answers=ground_truths, predicted_answers=predictions)
+        result = evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
 
         assert len(result) == 2
         assert result["score"] == pytest.approx(1.0)
@@ -112,7 +112,7 @@ def test_run_with_single_prediction(self):
         ground_truths = ["US $2.3 billion"]
         evaluator.warm_up()
         result = evaluator.run(
-            ground_truths_answers=ground_truths, predicted_answers=["A construction budget of US $2.3 billion"]
+            ground_truth_answers=ground_truths, predicted_answers=["A construction budget of US $2.3 billion"]
         )
         assert len(result) == 2
         assert result["score"] == pytest.approx(0.689089, abs=1e-5)
@@ -132,7 +132,7 @@ def test_run_with_mismatched_predictions(self):
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
         evaluator.warm_up()
-        result = evaluator.run(ground_truths_answers=ground_truths, predicted_answers=predictions)
+        result = evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
         assert len(result) == 2
         assert result["score"] == pytest.approx(0.8227189)
         assert result["individual_scores"] == pytest.approx([0.689089, 0.870389, 0.908679], abs=1e-5)
@@ -151,7 +151,7 @@ def test_run_with_bi_encoder_model(self):
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
         evaluator.warm_up()
-        result = evaluator.run(ground_truths_answers=ground_truths, predicted_answers=predictions)
+        result = evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
         assert len(result) == 2
         assert result["score"] == pytest.approx(1.0)
         assert result["individual_scores"] == pytest.approx([1.0, 1.0, 1.0])
@@ -170,7 +170,7 @@ def test_run_with_cross_encoder_model(self):
             "The Meiji Restoration in 1868 transformed Japan into a modernized world power.",
         ]
         evaluator.warm_up()
-        result = evaluator.run(ground_truths_answers=ground_truths, predicted_answers=predictions)
+        result = evaluator.run(ground_truth_answers=ground_truths, predicted_answers=predictions)
         assert len(result) == 2
         assert result["score"] == pytest.approx(0.999967, abs=1e-5)
         assert result["individual_scores"] == pytest.approx(