[skipci] [Doctest] added contrib regression (FractionalBias, FractionalAbsoluteError, CanberraMetric, GeometricMeanAbsoluteError) (pytorch#2323)

* added doctest for contrib regression metrics

* removed Engine definition and variable name changes

* added import of contrib.regression

Co-authored-by: Sylvain Desroziers <[email protected]>

Author: Ishan-Kumar2

docs/source/conf.py

@@ -339,6 +339,7 @@ def run(self):
 from ignite.handlers import *
 from ignite.metrics import *
 from ignite.utils import *
+from ignite.contrib.metrics.regression import *
 
 from ignite.contrib.metrics import *
 

ignite/contrib/metrics/regression/canberra_metric.py

@@ -37,6 +37,18 @@ class CanberraMetric(_BaseRegression):
     .. _`Botchkarev 2018`:
         https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf
 
+    .. testcode::
+
+            metric = CanberraMetric()
+            metric.attach(default_evaluator, 'canberra')
+            y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]])
+            y_true = y_pred * 1.5
+            state = default_evaluator.run([[y_pred, y_true]])
+            print(state.metrics['canberra'])
+
+    .. testoutput::
+
+            0.8000...
     .. versionchanged:: 0.4.3
 
         - Fixed implementation: ``abs`` in denominator.

ignite/contrib/metrics/regression/fractional_absolute_error.py

@@ -34,6 +34,18 @@ class FractionalAbsoluteError(_BaseRegression):
             metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
             non-blocking. By default, CPU.
 
+    .. testcode::
+
+            metric = FractionalAbsoluteError()
+            metric.attach(default_evaluator, 'fractional_abs_error')
+            y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]])
+            y_true = y_pred * 0.8
+            state = default_evaluator.run([[y_pred, y_true]])
+            print(state.metrics['fractional_abs_error'])
+
+    .. testoutput::
+
+            0.2222...
     .. versionchanged:: 0.4.5
         - Works with DDP.
     """

ignite/contrib/metrics/regression/fractional_bias.py

@@ -34,6 +34,19 @@ class FractionalBias(_BaseRegression):
             metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
             non-blocking. By default, CPU.
 
+    .. testcode::
+
+            metric = FractionalBias()
+            metric.attach(default_evaluator, 'fractional_bias')
+            y_pred = torch.Tensor([[3.8], [9.9], [5.4], [2.1]])
+            y_true = y_pred * 1.5
+            state = default_evaluator.run([[y_pred, y_true]])
+            print(state.metrics['fractional_bias'])
+
+    .. testoutput::
+
+            0.4000...
+
     .. versionchanged:: 0.4.5
         - Works with DDP.
     """

ignite/contrib/metrics/regression/geometric_mean_absolute_error.py

@@ -34,6 +34,18 @@ class GeometricMeanAbsoluteError(_BaseRegression):
             metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
             non-blocking. By default, CPU.
 
+    .. testcode::
+
+            metric = GeometricMeanAbsoluteError()
+            metric.attach(default_evaluator, 'gmae')
+            y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]])
+            y_true = y_pred * 1.5
+            state = default_evaluator.run([[y_pred, y_true]])
+            print(state.metrics['gmae'])
+
+    .. testoutput::
+
+            2.2723...
     .. versionchanged:: 0.4.5
         - Works with DDP.
     """