8000 [skip ci] Doctests for `MeanAbsoluteError` and `MeanSquaredError` by DevPranjal · Pull Request #2280 · pytorch/ignite · GitHub
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[skip ci] Doctests for MeanAbsoluteError and MeanSquaredError #2280

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Merged
merged 5 commits into from
Oct 20, 2021
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[skip ci] Doctests for MeanAbsoluteError and MeanSquaredError #2280

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2cc89cc
Add doctests for MeanAbsoluteError and MeanSquaredError
DevPranjal Oct 19, 2021
8c466a7
Change test output to lower precision
DevPranjal Oct 19, 2021
cb74ced
Update doctests to remove random values
DevPranjal Oct 19, 2021
acba3fa
Merge branch 'master' into metrics-doctest
sdesrozis Oct 20, 2021
3f91d7d
Merge branch 'master' into metrics-doctest
vfdev-5 Oct 20, 2021
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30 changes: 30 additions & 0 deletions ignite/metrics/mean_absolute_error.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,36 @@ class MeanAbsoluteError(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::

def process_function(engine, batch):
y_pred, y = batch
return y_pred, y
engine = Engine(process_function)
metric = MeanAbsoluteError()
metric.attach(engine, 'mae')
preds = torch.Tensor([
[1, 2, 4, 1],
[2, 3, 1, 5],
[1, 3, 5, 1],
[1, 5, 1 ,11]
])
target = preds * 0.75
state = engine.run([[preds, target]])
print(state.metrics['mae'])

.. testoutput::

2.9375
"""

@reinit__is_reduced
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30 changes: 30 additions & 0 deletions ignite/metrics/mean_squared_error.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,36 @@ class MeanSquaredError(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::

def process_function(engine, batch):
y_pred, y = batch
return y_pred, y
engine = Engine(process_function)
metric = MeanSquaredError()
metric.attach(engine, 'mse')
preds = torch.Tensor([
[1, 2, 4, 1],
[2, 3, 1, 5],
[1, 3, 5, 1],
[1, 5, 1 ,11]
])
target = preds * 0.75
state = engine.run([[preds, target]])
print(state.metrics['mse'])

.. testoutput::

3.828125
"""

@reinit__is_reduced
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0