Switch plotting order for pr curves (#2183)

CHANGELOG.md

@@ -30,6 +30,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Deprecated `metric._update_called` ([#2141](https://github.com/Lightning-AI/torchmetrics/pull/2141))
 
 
+- Changed x-/y-axis order for `PrecisionRecallCurve` to be consistent with scikit-learn ([#2183](https://github.com/Lightning-AI/torchmetrics/pull/2183))
+
+
 ### Removed
 
 -

src/torchmetrics/classification/precision_recall_curve.py

@@ -210,13 +210,16 @@ def plot(
 
         """
         curve_computed = curve or self.compute()
+        # switch order as the standard way is recall along x-axis and precision along y-axis
+        curve_computed = (curve_computed[1], curve_computed[0], curve_computed[2])
+
         score = (
             _auc_compute_without_check(curve_computed[0], curve_computed[1], 1.0)
             if not curve and score is True
             else None
         )
         return plot_curve(
-            curve_computed, score=score, ax=ax, label_names=("Precision", "Recall"), name=self.__class__.__name__
+            curve_computed, score=score, ax=ax, label_names=("Recall", "Precision"), name=self.__class__.__name__
         )
 
 
@@ -408,11 +411,13 @@ def plot(
 
         """
         curve_computed = curve or self.compute()
+        # switch order as the standard way is recall along x-axis and precision along y-axis
+        curve_computed = (curve_computed[1], curve_computed[0], curve_computed[2])
         score = (
             _reduce_auroc(curve_computed[0], curve_computed[1], average=None) if not curve and score is True else None
         )
         return plot_curve(
-            curve_computed, score=score, ax=ax, label_names=("Precision", "Recall"), name=self.__class__.__name__
+            curve_computed, score=score, ax=ax, label_names=("Recall", "Precision"), name=self.__class__.__name__
         )
 
 
@@ -598,11 +603,13 @@ def plot(
 
         """
         curve_computed = curve or self.compute()
+        # switch order as the standard way is recall along x-axis and precision along y-axis
+        curve_computed = (curve_computed[1], curve_computed[0], curve_computed[2])
         score = (
             _reduce_auroc(curve_computed[0], curve_computed[1], average=None) if not curve and score is True else None
         )
         return plot_curve(
-            curve_computed, score=score, ax=ax, label_names=("Precision", "Recall"), name=self.__class__.__name__
+            curve_computed, score=score, ax=ax, label_names=("Recall", "Precision"), name=self.__class__.__name__
         )