Doctests for RMSE and MeanPairwiseDistance (#2307)

* Add doctests for RMSE and MeanPairwiseDistance

* Add doctests for RMSE and MeanPairwiseDistance

* Make metric name consistent

Author: DevPranjal

docs/source/conf.py

@@ -341,6 +341,14 @@ def run(self):
 from ignite.utils import *
 
 manual_seed(666)
+
+# create default evaluator for doctests
+
+def process_function(engine, batch):
+    y_pred, y = batch
+    return y_pred, y
+
+default_evaluator = Engine(process_function)
 """
 
 

ignite/metrics/mean_pairwise_distance.py

@@ -26,6 +26,32 @@ class MeanPairwiseDistance(Metric):
         device: specifies which device updates are accumulated on. Setting the
             metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
             non-blocking. By default, CPU.
+
+    Examples:
+        To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
+        The output of the engine's ``process_function`` needs to be in the format of
+        ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added
+        to the metric to transform the output into the form expected by the metric.
+
+        ``y_pred`` and ``y`` should have the same shape.
+
+        .. testcode::
+
+            metric = MeanPairwiseDistance(p=4)
+            metric.attach(default_evaluator, 'mpd')
+            preds = torch.Tensor([
+                [1, 2, 4, 1],
+                [2, 3, 1, 5],
+                [1, 3, 5, 1],
+                [1, 5, 1 ,11]
+            ])
+            target = preds * 0.75
+            state = default_evaluator.run([[preds, target]])
+            print(state.metrics['mpd'])
+
+        .. testoutput::
+
+            1.5955...
     """
 
     def __init__(

ignite/metrics/root_mean_squared_error.py

@@ -26,6 +26,32 @@ class RootMeanSquaredError(MeanSquaredError):
         device: specifies which device updates are accumulated on. Setting the
             metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
             non-blocking. By default, CPU.
+
+    Examples:
+        To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
+        The output of the engine's ``process_function`` needs to be in the format of
+        ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added
+        to the metric to transform the output into the form expected by the metric.
+
+        ``y_pred`` and ``y`` should have the same shape.
+
+        .. testcode::
+
+            metric = RootMeanSquaredError()
+            metric.attach(default_evaluator, 'rmse')
+            preds = torch.Tensor([
+                [1, 2, 4, 1],
+                [2, 3, 1, 5],
+                [1, 3, 5, 1],
+                [1, 5, 1 ,11]
+            ])
+            target = preds * 0.75
+            state = default_evaluator.run([[preds, target]])
+            print(state.metrics['rmse'])
+
+        .. testoutput::
+
+            1.956559480312316
     """
 
     def compute(self) -> Union[torch.Tensor, float]: