add k param

README.md

@@ -31,6 +31,7 @@ It is the average of the precision scores computer after each relevant document
 ### Inputs
 - **predictions:** a list of dictionaries where each dictionary consists of document relevancy scores produced by the model for a given query. One dictionary per query. The dictionaries should be converted to string. 
 - **references:** a lift of list of dictionaries where each dictionary consists of the relevant order for the documents for a given query in a sorted relevancy order. The dictionaries should be converted to string.
+- **k:**  an optional paramater whose default is None to calculate map@k
 
 ### Output Values
 - **map (`float`):** mean average precision score. Minimum possible value is 0. Maximum possible value is 1.0

map.py

@@ -46,6 +46,7 @@ Args:
         One dictionary per query.  
     references: List of list of strings where each lists consists of the relevant document names for a given query in a sorted relevancy order.
         The outer list is sorted from query one to n.
+    k: `int`, optional, default is None, it is to calculate map@k
 Returns:
     map (`float`): mean average precision score. Minimum possible value is 0. Maximum possible value is 1.0
 Examples:
@@ -75,14 +76,15 @@ class map(evaluate.Metric):
             inputs_description=_KWARGS_DESCRIPTION,
             # This defines the format of each prediction and reference
             features=datasets.Features({
-                'predictions':  datasets.Value("string"), #list[dict],
-                'references':  datasets.Value("string")#datasets.Sequence(datasets.Sequence(datasets.Value("string"))), #list[list[str]],
+                'predictions':  datasets.Value("string"),
+                'references':  datasets.Value("string"),
+                'k': datasets.Value("int", default=None)    
             }),
             # Homepage of the module for documentation
             reference_urls=["https://amenra.github.io/ranx/"]
         )
 
-    def _compute(self, predictions, references):
+    def _compute(self, predictions, references, k=None):
         """Returns the scores"""
         preds = {}
         refs = {}
@@ -92,12 +94,6 @@ class map(evaluate.Metric):
             refs = refs | json.loads(ref)
         
         run = Run(preds)
-        """gt_dict = {}
-        for i in range(len(references)):
-            per_query_gt = {}
-            for rank in range(len(references[i])):
-                per_query_gt[references[i][rank]] = rank+1
-            gt_dict[f"q_{i+1}"] = per_query_gt"""
         qrels = Qrels(refs)
         map_score = ran_evaluate(qrels, run, "map")
         return {