Add empty test

.gitignore

@@ -0,0 +1 @@
+__pycache__

mean_average_precision.py

@@ -70,15 +70,17 @@ class mean_average_precision(evaluate.Metric):
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
             # This defines the format of each prediction and reference
-            features=datasets.Features({
-                'predictions': datasets.Value('int64'),
-                'references': datasets.Value('int64'),
-            }),
+            features=datasets.Features(
+                {
+                    "predictions": datasets.Value("int64"),
+                    "references": datasets.Value("int64"),
+                }
+            ),
             # Homepage of the module for documentation
             homepage="http://module.homepage",
             # Additional links to the codebase or references
             codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
-            reference_urls=["http://path.to.reference.url/new_module"]
+            reference_urls=["http://path.to.reference.url/new_module"],
         )
 
     def _download_and_prepare(self, dl_manager):
@@ -86,10 +88,5 @@ class mean_average_precision(evaluate.Metric):
         # TODO: Download external resources if needed
         pass
 
-    def _compute(self, predictions, 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)
-        return {
-            "accuracy": accuracy,
-        }
+    def _compute(self, predictions, references, **kwargs):
+        return {"mean_average_precision": 1.0}

tests.py

@@ -2,16 +2,18 @@ test_cases = [
     {
         "predictions": [0, 0],
         "references": [1, 1],
-        "result": {"metric_score": 0}
+        "result": {"mean_average_precision": 1.0},
     },
-    {
-        "predictions": [1, 1],
-        "references": [1, 1],
-        "result": {"metric_score": 1}
-    },
-    {
-        "predictions": [1, 0],
-        "references": [1, 1],
-        "result": {"metric_score": 0.5}
-    }
-]
+]
+
+from mean_average_precision import mean_average_precision
+
+m = mean_average_precision()
+
+for test_case in test_cases:
+    assert (
+        m.compute(
+            predictions=test_case["predictions"], references=test_case["references"]
+        )
+        == test_case["result"]
+    )