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Addresses #2265

Description:
added doctests for contrib metrics AveragePrecision, PrecisionRecallCurve, ROC_AUC and RocCurve
Not sure about the list of floats outputs, please let me know if there is a better way to handle those.
@ydcjeff @sdesrozis

Check list:

  • New tests are added (if a new feature is added)
  • New doc strings: description and/or example code are in RST format
  • Documentation is updated (if required)

@github-actions github-actions bot added docs module: contrib Contrib module labels Dec 5, 2021
@sdesrozis
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@Ishan-Kumar2 Thanks a lot for your help ! I left some minor comments.

avg_precision = AveragePrecision(activated_output_transform)
avg_precision = AveragePrecision()
#The ``output_transform`` arg of the metric can be used to perform a softmax on the ``y_pred``.
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@sdesrozis sdesrozis Dec 5, 2021

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Thinking about it, it is worth to keep the current example to show the way to respect the data format, maybe as a note or an example following the one you did. What do you think ?

Note:
     AveragePrecision expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below:

        .. code-block:: python

            def activated_output_transform(output):
                y_pred, y = output
                y_pred = torch.softmax(y_pred, dim=1)
                return y_pred, y

            avg_precision = AveragePrecision(activated_output_transform)

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Yup, makes sense to have it in the docs somewhere, I have added it now!

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@Ishan-Kumar2 Thank you ! LGTM

@sdesrozis sdesrozis merged commit 71bbe2f into pytorch:master Dec 6, 2021
@sdesrozis sdesrozis mentioned this pull request Dec 6, 2021
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Ishan-Kumar2 added a commit to Ishan-Kumar2/ignite that referenced this pull request Dec 26, 2021
…isionRecallCurve``, ``ROC_AUC`` and ``RocCurve`` (pytorch#2341)
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2 participants