Update _classification.py

ivy/functional/frontends/sklearn/metrics/_classification.py

@@ -20,7 +20,7 @@ def accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None):
 
 
 @to_ivy_arrays_and_back
-def precision_score(y_true, y_pred, *, sample_weight=None):
+def recall_score(y_true, y_pred, *, sample_weight=None):
     # Ensure that y_true and y_pred have the same shape
     if y_true.shape != y_pred.shape:
         raise IvyValueError("y_true and y_pred must have the same shape")
@@ -29,24 +29,20 @@ def precision_score(y_true, y_pred, *, sample_weight=None):
     if sample_weight is not None:
         sample_weight = ivy.array(sample_weight)
         if sample_weight.shape[0] != y_true.shape[0]:
-            raise IvyValueError(
-                "sample_weight must have the same length as y_true and y_pred"
-            )
+            raise IvyValueError("sample_weight must have the same length as y_true and y_pred")
         sample_weight = sample_weight / ivy.sum(sample_weight)
     else:
         sample_weight = ivy.ones_like(y_true)
 
-    # Calculate true positives and predicted positives
-    true_positives = ivy.logical_and(ivy.equal(y_true, 1), ivy.equal(y_pred, 1)).astype(
-        "int64"
-    )
-    predicted_positives = ivy.equal(y_pred, 1).astype("int64")
+    # Calculate true positives and actual positives
+    true_positives = ivy.logical_and(ivy.equal(y_true, 1), ivy.equal(y_pred, 1)).astype("int64")
+    actual_positives = ivy.equal(y_true, 1).astype("int64")
 
     # Apply sample weights
     weighted_true_positives = ivy.multiply(true_positives, sample_weight)
-    weighted_predicted_positives = ivy.multiply(predicted_positives, sample_weight)
+    weighted_actual_positives = ivy.multiply(actual_positives, sample_weight)
 
-    # Compute precision
-    ret = ivy.sum(weighted_true_positives) / ivy.sum(weighted_predicted_positives)
+    # Compute recall
+    ret = ivy.sum(weighted_true_positives) / ivy.sum(weighted_actual_positives)
     ret = ret.astype("float64")
     return ret