Adding the target perplexity fix back (#15)

---------

Co-authored-by: Thomas Wolf <[email protected]>

src/lighteval/metrics/__init__.py

@@ -8,11 +8,18 @@
 
 def apply_target_perplexity_metric(results: list[ModelReturn], formatted_doc: Doc, metrics: list[str]):
     outputs = {}
-    current_results = [results.pop(0) for _ in range(len(formatted_doc.get_golds()))]
+    reference_text = formatted_doc.get_golds()[0]
+    current_result = results.pop(0)
+    target_logprob = current_result.result[0]
+    target_acc = current_result.result[1]
 
     for metric in metrics:
-        if Metrics[metric].value.category == MetricCategory.PERPLEXITY:
-            outputs.update(Metrics[metric].value.compute(results=current_results))
+        if Metrics[metric].value.category == MetricCategory.TARGET_PERPLEXITY:
+            outputs.update(
+                Metrics[metric].value.compute(
+                    logprobs=target_logprob, target_acc=target_acc, reference_text=reference_text
+                )
+            )
 
     return results, outputs
 
@@ -30,7 +37,9 @@ def apply_perplexity_metric(results: list[ModelReturn], formatted_doc: Doc, metr
 
     for metric in metrics:
         if Metrics[metric].value.category == MetricCategory.PERPLEXITY:
-            outputs.update(Metrics[metric].value.compute(results=current_result, reference_text=reference_text))
+            outputs.update(
+                Metrics[metric].value.compute(logprobs=current_result.result, reference_text=reference_text)
+            )
 
     return results, outputs
 

src/lighteval/metrics/metrics_sample.py

@@ -1,6 +1,8 @@
 """This module manages all the metrics occurring at the sample level. The results of said metrics are then aggregated
 using simple function (min, mean, max, ...) at the corpus level. Most metrics fall under this category.
 """
+from typing import Union
+
 import nltk
 import numpy as np
 from nltk.metrics.distance import edit_distance
@@ -275,17 +277,16 @@ def compute(self, choices_logprob: list[float], gold_ixs: list[float], formatted
         return 1.0 / (min(ranked_choices) + 1)
 
 
-def acc_golds_likelihood(results: list[tuple[float, int]], **kwargs) -> int:
+def acc_golds_likelihood(target_acc: Union[list[int], int], **kwargs) -> int:
     """Tests if at least one of predicted gold targets' log-likelihood is above 0.5.
 
     Args:
-        results (list[int]): List of tuples containing, for each gold, the predictions log-probabilities associated with whether they are above 0.5 aggregated.
-        formatted_doc (Doc): _description_
+        target_acc (list[int]): List of scores indicating whether the predictions log-probabilities are above 0.5 aggregated.
 
     Returns:
         int: 1 if at least one of the possible golds had a log-likelihood above 0.5.
     """
-    return max([int(acc_ppl) for _, acc_ppl in results])
+    return max([int(acc_ppl) for acc_ppl in as_list(target_acc)])
 
 
 class ROUGE:

src/lighteval/metrics/sample_preparator.py

@@ -106,14 +106,14 @@ def count_units(self, text: str) -> int:
         if self.units_type == "bytes":
             return len(text.encode("utf-8"))
 
-    def prepare(self, results, reference_text, **kwargs):
+    def prepare(self, logprobs: list[float] | float, reference_text: str, **kwargs):
         """Prepares an individual perplexity example to the format expected by metrics computed at the corpus level (aggregated).
 
         Args:
-            results (list[float]): List of the logprobabilities computed for each item
+            logprobs (list[float]): List of the logprobabilities computed for each item of the sequence or single aggregated logprob over the sequence
             reference_text (str): Current reference text for which to compute the length in self.units_type
 
         Returns:
             PerplexityCorpusMetricInput: Stores the measured logprobs and associated text lengths, counted in the reference unit.
         """
-        return PerplexityCorpusMetricInput(logprobs=results.result, weights=self.count_units(reference_text))
+        return PerplexityCorpusMetricInput(logprobs=logprobs, weights=self.count_units(reference_text))