Add compute code

pr_auc.py

@@ -15,6 +15,7 @@
 
 import evaluate
 import datasets
+from sklearn.metrics import precision_recall_curve, auc
 
 
 # TODO: Add BibTeX citation
@@ -28,7 +29,7 @@ year={2020}
 
 # TODO: Add description of the module here
 _DESCRIPTION = """\
-This new module is designed to solve this great ML task and is crafted with a lot of care.
+Computes the area under precision-recall curve. Implementation details taken from https://sinyi-chou.github.io/python-sklearn-precision-recall/
 """
 
 
@@ -86,10 +87,10 @@ class PRAUC(evaluate.Metric):
         # TODO: Download external resources if needed
         pass
 
-    def _compute(self, predictions, references):
+    def _compute(self, prediction_scores, references):
         """Returns the scores"""
-        # TODO: Compute the different scores of the module
-        accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions)
+        precision, recall, thresholds = precision_recall_curve(references, prediction_scores)
+        auc_precision_recall = auc(recall, precision)
         return {
-            "accuracy": accuracy,
+            "pr_auc": auc_precision_recall,
         }