Fixing naming for sample evals + adding reqs in aime24

docs/source/package_reference/metrics.mdx

@@ -56,15 +56,21 @@
 [[autodoc]] metrics.metrics_sample.BLEU
 ### StringDistance
 [[autodoc]] metrics.metrics_sample.StringDistance
+
+### Metrics allowing sampling
+#### PassAtK
+[[autodoc]] metrics.metrics_sample.PassAtK
+#### MajAtN
+[[autodoc]] metrics.metrics_sample.MajAtN
+#### AvgAtN
+[[autodoc]] metrics.metrics_sample.AvgAtN
+
+## LLM-as-a-Judge
+### JudgeLM
+[[autodoc]] metrics.utils.llm_as_judge.JudgeLM
 ### JudgeLLM
 [[autodoc]] metrics.metrics_sample.JudgeLLM
 ### JudgeLLMMTBench
 [[autodoc]] metrics.metrics_sample.JudgeLLMMTBench
 ### JudgeLLMMixEval
 [[autodoc]] metrics.metrics_sample.JudgeLLMMixEval
-### MajAtK
-[[autodoc]] metrics.metrics_sample.MajAtK
-
-## LLM-as-a-Judge
-### JudgeLM
-[[autodoc]] metrics.utils.llm_as_judge.JudgeLM

src/lighteval/metrics/metrics.py

@@ -41,7 +41,7 @@
     MRR,
     ROUGE,
     AccGoldLikelihood,
-    AvgAtK,
+    AvgAtN,
     BertScore,
     ExactMatches,
     Extractiveness,
@@ -50,7 +50,7 @@
     GPassAtK,
     JudgeLLMSimpleQA,
     LoglikelihoodAcc,
-    MajAtK,
+    MajAtN,
     PassAtK,
     Recall,
     StringDistance,
@@ -85,16 +85,16 @@ class Metrics(Enum):
         corpus_level_fn=np.mean,
         higher_is_better=True,
     )
-    avg_at_k = SampleLevelMetric(
-        metric_name="avg@k",
-        sample_level_fn=AvgAtK(strip_strings=True),
+    avg_at_n = SampleLevelMetric(
+        metric_name="avg@n",
+        sample_level_fn=AvgAtN(strip_strings=True),
         category=SamplingMethod.GENERATIVE,
         corpus_level_fn=np.mean,
         higher_is_better=True,
     )
-    avg_at_k_math = SampleLevelMetric(
-        metric_name="avg@k",
-        sample_level_fn=AvgAtK(
+    avg_at_n_math = SampleLevelMetric(
+        metric_name="avg@n",
+        sample_level_fn=AvgAtN(
             sample_scoring_function=MultilingualExtractiveMatchMetric(
                 language=Language.ENGLISH,
                 gold_extraction_target=[ExprExtractionConfig(), LatexExtractionConfig()],
@@ -365,9 +365,9 @@ class Metrics(Enum):
         corpus_level_fn=CorpusLevelF1Score(None),
         higher_is_better=True,
     )
-    maj_at_k = SampleLevelMetric(
-        metric_name="maj@k",
-        sample_level_fn=MajAtK(),
+    maj_at_n = SampleLevelMetric(
+        metric_name="maj@n",
+        sample_level_fn=MajAtN(),
         category=SamplingMethod.GENERATIVE,
         corpus_level_fn=np.mean,
         higher_is_better=True,

src/lighteval/metrics/metrics_sample.py

@@ -63,7 +63,7 @@
 
 class SampleLevelComputation(ABC):
     @abstractmethod
-    def compute(self, model_response: ModelResponse, doc: Doc, **kwargs):
+    def compute(self, doc: Doc, model_response: ModelResponse, **kwargs):
         raise NotImplementedError
 
     def __str__(self):
@@ -444,7 +444,7 @@ def __init__(self, length_normalization: bool = False):
         """
         self.length_normalization = length_normalization
 
-    def compute(self, model_response: ModelResponse, doc: Doc, **kwargs) -> float:
+    def compute(self, doc: Doc, model_response: ModelResponse, **kwargs) -> float:
         """Mean reciprocal rank. Measures the quality of a ranking of choices (ordered by correctness).
 
         Args:
@@ -1129,14 +1129,13 @@ def __init__(
                 raise ValueError(f"Unknown normalization function: {normalize}")
         else:
             self.normalize = normalize
-
         self.strip_strings = strip_strings
 
         if callable(sample_scoring_function):
             self.compute_score = sample_scoring_function
             self.type_exact_match = None
         elif isinstance(sample_scoring_function, SampleLevelComputation):
-            self.score_sample = sample_scoring_function.compute
+            self.compute_score = sample_scoring_function.compute
             self.type_exact_match = None
         else:
             if isinstance(sample_scoring_function, str):
@@ -1145,11 +1144,9 @@ def __init__(
                         f"type_exact_match (used in parametrized_exact_match) must be one of prefix, suffix, or full. Was {sample_scoring_function} instead."
                     )
                 self.type_exact_match = sample_scoring_function
-                self.score_sample = self.default_sample_scoring
             else:
                 self.type_exact_match = "full"
             self.compute_score = self.default_sample_scoring
-            self.score_sample = self.default_sample_scoring
 
     def preprocess(self, text: str) -> str:
         if not text:
@@ -1176,19 +1173,19 @@ def name_metrics(self) -> str | list[str]:
         raise NotImplementedError
 
 
-class AvgAtK(SamplingMetric, SampleLevelComputation):
-    def __init__(self, k: int | None = None, **kwargs):
-        """Sample score averages all the individual k predictions scores.
+class AvgAtN(SamplingMetric, SampleLevelComputation):
+    def __init__(self, n: int | None = None, **kwargs):
+        """Sample score averages all the individual n predictions scores.
 
         Args:
-            k (int | None): The number of top choices to consider.
+            n (int | None): Number of samples to generate
             **kwargs: Additional keyword arguments.
         """
         super().__init__(**kwargs)
-        self.k = k
-        self.attribute_must_be_set = ["k"]
+        self.n = n
+        self.attribute_must_be_set = ["n"]
 
-    def compute(self, model_response: ModelResponse, doc: Doc, **kwargs):
+    def compute(self, doc: Doc, model_response: ModelResponse, **kwargs):
         """Computes the metric over a list of golds and predictions for one single sample.
         It applies normalisation (if needed) to model prediction and gold, and takes the most frequent answer of all the available ones,
         then compares it to the gold.
@@ -1203,36 +1200,32 @@ def compute(self, model_response: ModelResponse, doc: Doc, **kwargs):
         """
         all_scores = []
         for i in range(self.k):
-            all_scores.append(self.score_sample(doc, model_response[i]))
+            all_scores.append(self.compute_score(doc, model_response[i]))
 
         avg_score = np.mean(all_scores)
         return avg_score
 
     def num_samples(self):
-        """Get the number of samples for this metric.
-
-        Returns:
-            int: The number of samples
-        """
-        return self.k
+        return self.n
 
 
-class MajAtK(SamplingMetric, SampleLevelComputation):
-    def __init__(self, k: int | None = None, **kwargs):
+class MajAtN(SamplingMetric, SampleLevelComputation):
+    def __init__(self, n: int | None = None, **kwargs):
         """An exact match class.
 
         Args:
-            k (int): The number of top choices to consider.
+            n (int): Total number of samples to generate
             **kwargs: Additional keyword arguments.
         """
         super().__init__(**kwargs)
 
-        self.k = k
-        self.attribute_must_be_set = ["k"]
+        self.n = n
+        self.attribute_must_be_set = ["n"]
 
     def compute(self, doc: Doc, model_response: ModelResponse, **kwargs):
         """Computes the metric over a list of golds and predictions for one single sample.
-        It applies normalisation (if needed) to model prediction and gold, and takes the most frequent answer of all the available ones, then compares it to the gold.
+        It applies normalisation (if needed) to model prediction and gold, and takes the most frequent answer of all the available ones,
+        then compares it to the gold.
 
         Args:
             doc (Doc): The document containing gold references.
@@ -1243,39 +1236,38 @@ def compute(self, doc: Doc, model_response: ModelResponse, **kwargs):
             float: Aggregated score over the current sample's items.
         """
         if self.k is None:
-            raise Exception("You did not set the value of k")
+            raise Exception("You did not set the value of n")
 
         golds = doc.get_golds()
-
         if len(golds) > 1:
-            raise Exception("Cannot compute maj@k with several golds")
+            raise Exception("Cannot compute maj@n with several golds")
 
         processed_choices = [self.preprocess(text=g) for g in doc.get_golds()]
         new_doc = Doc(
             choices=processed_choices,
             query=doc.query,
-            gold_index=list(range(len(processed_choices))),
+            gold_index=doc.gold_index,
         )
         all_answers = []
-        for pred in model_response.final_text[: self.k]:
+        for pred in model_response.final_text[: self.n]:
             all_answers.append(self.preprocess(text=pred))
         majority_prediction = max(all_answers, key=all_answers.count)
         new_model_response = ModelResponse(
             text=[majority_prediction],
         )
-        return self.compute_score(new_doc, new_model_response)
+        return self.compute_score(new_model_response, new_doc)
 
     def num_samples(self):
-        return self.k
+        return self.n
 
 
 class PassAtK(SamplingMetric, SampleLevelComputation):
     def __init__(self, k: int | None = None, n: int | None = None, **kwargs):
-        """Computing pass at k
+        """Computing pass at k with an estimator
 
         Args:
-            k (int | None): Threshold for the number of successful attempts.
-            n (int | None): Number of samples to generate.
+            k (int | None): Number of correct samples threshold
+            n (int | None): Total number of samples to generate.
             **kwargs: Additional keyword arguments.
         """
         super().__init__(**kwargs)
@@ -1320,7 +1312,7 @@ def compute(self, doc: Doc, model_response: ModelResponse, **kwargs) -> float:
             new_model_response = ModelResponse(
                 text=[cur_pred],
             )
-            all_scores.append(self.score_sample(doc=new_doc, model_response=new_model_response))
+            all_scores.append(self.compute_score(doc=new_doc, model_response=new_model_response))
 
         return self.pass_at_k(all_scores)
 
@@ -1348,8 +1340,8 @@ def __init__(
         """Computing G-Pass@k from http://arxiv.org/abs/2412.13147
 
         Args:
-            k (Union[int, list[int]] | None): The number of successful attempts to be considered.
-            n (int | None): Number of samples to generate.
+            k (Union[int, list[int]] | None): Number of correct samples threshold
+            n (int | None): Total number of samples to generate.
             thresholds (list[float]): Thresholds to control successful attempts in k generate.
             name_prefix (str | None): Prefix for the metric name.
             **kwargs: Additional keyword arguments.
@@ -1370,7 +1362,7 @@ def k(self):
     def k(self, new_val):
         self._k = as_list(new_val)
 
-    def compute(self, model_response: ModelResponse, doc: Doc, **kwargs) -> float:
+    def compute(self, doc: Doc, model_response: ModelResponse, **kwargs):
         """Computes the metric over a list of golds and predictions for one single item with possibly many samples.
         It applies normalisation (if needed) to model prediction and gold, computes their per prediction score,
         then aggregates the scores over the samples using a pass@k.
@@ -1410,7 +1402,7 @@ def compute(self, model_response: ModelResponse, doc: Doc, **kwargs) -> float:
             new_model_response = ModelResponse(
                 text=[cur_pred],
             )
-            all_scores.append(self.score_sample(new_doc, new_model_response))
+            all_scores.append(self.compute_score(new_doc, new_model_response))
 
         return self.g_pass_at_k(all_scores)
 

src/lighteval/metrics/utils/metric_utils.py

@@ -89,7 +89,7 @@ def __call__(self, sample_params: dict | None):
         sample_params_name = "&".join(f"{k}={v}" for k, v in sample_params.items())
         if isinstance(self, MetricGrouping):
             if hasattr(self.sample_level_fn, "metric_names"):
-                # this is mostly for the gpass@k metrics
+                # this is mostly for the gpass@k metrics which redefine submetric names
                 self.metric_name = self.sample_level_fn.metric_names
             else:
                 self.metric_name = [f"{metric}:{sample_params_name}" for metric in self.metric_name]