evaluator WIP

Project_Model/Libs/Evaluation/evaluation.py

@@ -0,0 +1,37 @@
+import evaluate
+
+class Evaluator():
+    def __init__(self) -> None:
+        # txt based evaluator
+        self.__rouge = evaluate.load("rouge")
+        self.__rouge_types = ["rougeLsum", "rouge1", "rouge2"] #rougeLsum will work bad because it expect that each sentence are divided with /n
+
+        self._bleu = evaluate.load("bleu")
+        self._meteor = evaluate.load("meteor")
+
+    def rdf2txt_rouge_evaluation(self, preds: list[str], refs: list[str]):
+
+        results = self.__rouge.compute(
+        predictions=preds, references=refs,
+        rouge_types=self.__rouge_types,
+        use_stemmer=True, 
+        use_aggregator=True #F1
+        )
+
+        return {k: float(results[k]) for k in self.__rouge_types}
+    
+    def rdf2txt_bleu_evaluation(self, preds: list[str], refs: list[str]) -> float:
+        # sacreBLEU via evaluate; expects references as list-of-lists
+        # each prediction can be evaluated against a list of references, hence [[ref]]
+        results = self._bleu.compute(predictions=preds, references=[[r] for r in refs])
+        return float(results["bleu"])   # (native sacreBLEU scale)
+    
+    def rdf2txt_meteor_evaluation(self, preds: list[str], refs: list[str]) -> float:
+        # as bleu
+        res = self._meteor.compute(predictions=preds, references=[[r] for r in refs])
+        return float(res["meteor"])
+    
+
+    def txt2rdf_precision_evaluation(self,preds: list[str], refs: list[str]):
+
+