[Metrics] Add multiclass auroc #4236

ddrevicky · 2020-10-19T16:12:30Z

What does this PR do?

Implements functional multiclass AUROC.

Notes on the code:

Had to pass reorder=False for auc because tests against sklearn kept showing different values and debugging showed that the difference was actually coming out of our auc using torch.argsort which is unstable. A short colab notebook documenting this. I submitted a separate issue #4237 for that.

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ddrevicky · 2020-10-19T16:36:22Z

pytorch_lightning/metrics/functional/classification.py

+    @multiclass_auc_decorator(reorder=False)
+    def _multiclass_auroc(pred, target, sample_weight, num_classes):
+        return multiclass_roc(pred, target, sample_weight, num_classes)
+
+    class_aurocs = _multiclass_auroc(pred=pred, target=target,
+                                     sample_weight=sample_weight,
+                                     num_classes=num_classes)
+    return torch.mean(class_aurocs)


I've implemented this using the multiclass_auc_decorator similarly as is done for auroc but have to say that as not such an experienced Pythonist I was scratching my head for a good while trying to figure out what the multiclass_auc_decorator was doing. It's possible that for other people reading the code it might also take unnecessary amount of time. Would the following be more readable? No decorator is needed either outside or within the multiclass_auroc function. Just my humble opinion, which do you guys prefer? :)

class_rocs = multiclass_roc(pred=pred, target=target, sample_weight=sample_weight, num_classes=num_classes) class_aurocs = [] for fpr, tpr, _ in class_rocs: class_aurocs.append(auc(fpr, tpr, reorder=False)) return torch.mean(torch.stack(class_aurocs))

codecov · 2020-10-19T16:38:27Z

Codecov Report

Merging #4236 into master will increase coverage by 3%.
The diff coverage is 100%.

@@           Coverage Diff           @@
##           master   #4236    +/-   ##
=======================================
+ Coverage      90%     93%    +3%     
=======================================
  Files         113     113            
  Lines        8232    8191    -41     
=======================================
+ Hits         7387    7612   +225     
+ Misses        845     579   -266

CHANGELOG.md

tests/metrics/functional/test_classification.py

teddykoker

LGTM

Borda

lgtm

pytorch_lightning/metrics/functional/classification.py

tchaton

Great PR ! Some extra tests on matrix size would be great !

tchaton · 2020-10-20T13:12:32Z

pytorch_lightning/metrics/functional/classification.py

+            "Multiclass AUROC metric expects the target scores to be"
+            " probabilities, i.e. they should sum up to 1.0 over classes")
+
+    if torch.unique(target).size(0) != pred.size(1):


Shouldn't it be torch.unique(target).size(0) <= pred.size(1)

target = torch.tensor([0, 0, 0, 0]) >>> torch.unique(target).size(0) 1

Or we could have a get_num_classes utils too.

Well the metric is undefined when torch.unique(target).size(0) < pred.size(1), that's why there is a strict equals. The way this implementation works (based on sklearn's version) is, that it uses a one-vs-rest strategy of computing the AUROC. For n classes it computes n binary AUROCs, for each class in turn, it is considered a positive class and all the other classes negative. Then it averages those.

E.g., for n=3 and target=[0, 1, 1, 2], for class 0 we binarize the target to make 0 the positive class: [1, 0, 0, 0] and compute the AUC of ROC of that.

If a target label is not present in the target, e.g. n=3 and target=[0, 1, 1, 1] then for the absent class 2 the binarized target would look like [0, 0, 0, 0] (all negative) and ROC cannot be computed (would raise an error). Consequently, the whole multiclass AUROC is undefined in that case.

As for get_num_classes there already is such a util, but it does something different than we need here. It doesn't look at dimensions of the predictions, just at the max value in both and deduced num classes from that (which when I think about it now, could fail silently for example when n_cls=5 but target=[0,1,2,3].

Make sense !

tchaton · 2020-10-20T13:13:09Z

pytorch_lightning/metrics/functional/classification.py

+        >>> target = torch.tensor([0, 1, 3, 2])
+        >>> multiclass_auroc(pred, target)   # doctest: +NORMALIZE_WHITESPACE
+        tensor(0.6667)
+    """


Also, we should check pred.size(0) == target.size(0)

I can add that of course but this is not a check that is done in any other metric implementation. So if it's done here it should probably be done everywhere. If that's desired, I could add a helper to classification.py

def check_batch_dims(pred, target): if not pred.size(0) == target.size(0): raise ValueError(f"Batch size for prediction ({pred.size(0)}) and target ({target.size(0)}) must be equal.")

Would that work? Then this helper could be used in each metric instead of copy pasting the if clause and the exception.

As we are slowly unifying the functional and class based interface, we are doing more checks for shape, so this will come in a future PR :]

tchaton · 2020-10-28T12:21:43Z

pytorch_lightning/metrics/functional/classification.py

+            "Multiclass AUROC metric expects the target scores to be"
+            " probabilities, i.e. they should sum up to 1.0 over classes")
+
+    if torch.unique(target).size(0) != pred.size(1):


Make sense !

SkafteNicki · 2020-10-30T11:46:04Z

CHANGELOG.md

@@ -106,6 +106,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

 - Added trace functionality to the function `to_torchscript` ([#4142](https://github.com/PyTorchLightning/pytorch-lightning/pull/4142))

+- Added multiclass AUROC metric ([#4236](https://github.com/PyTorchLightning/pytorch-lightning/pull/4236))


@ddrevicky could you move this to the unreleased section?

Should be okay now.

* Add functional multiclass AUROC metric * Add multiclass_auroc tests * fixup! Add functional multiclass AUROC metric * fixup! fixup! Add functional multiclass AUROC metric * Add multiclass_auroc doc reference * Update CHANGELOG * formatting * Shorter error message regex match in tests * Set num classes as pytest parameter * formatting * Update CHANGELOG Co-authored-by: Jirka Borovec <[email protected]> Co-authored-by: Nicki Skafte <[email protected]> (cherry picked from commit 38bb4e2)

ddrevicky added 5 commits October 19, 2020 18:07

Add functional multiclass AUROC metric

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Add multiclass_auroc tests

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fixup! Add functional multiclass AUROC metric

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fixup! fixup! Add functional multiclass AUROC metric

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Add multiclass_auroc doc reference

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mergify bot requested a review from a team October 19, 2020 16:12

Update CHANGELOG

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ddrevicky changed the title ~~Feature/3304 multiclass auroc~~ [Metrics] Add multiclass auroc Oct 19, 2020

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ddrevicky added 2 commits October 20, 2020 09:45

Shorter error message regex match in tests

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Set num classes as pytest parameter

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formatting

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NumesSanguis mentioned this pull request Oct 23, 2020

error when using auroc #4293

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Merge branch 'master' into feature/3304-multiclass-auroc

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SkafteNicki added 3 commits October 28, 2020 16:43

Merge branch 'master' into feature/3304-multiclass-auroc

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Merge branch 'master' into feature/3304-multiclass-auroc

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Merge branch 'master' into feature/3304-multiclass-auroc

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Merge branch 'master' into feature/3304-multiclass-auroc

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[Metrics] Confusion matrix class interface #4348

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[Metrics] Add multiclass auroc #4236

[Metrics] Add multiclass auroc #4236

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		@@ -106,6 +106,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

		- Added trace functionality to the function `to_torchscript` ([#4142](https://github.com/PyTorchLightning/pytorch-lightning/pull/4142))

		- Added multiclass AUROC metric ([#4236](https://github.com/PyTorchLightning/pytorch-lightning/pull/4236))

[Metrics] Add multiclass auroc #4236

[Metrics] Add multiclass auroc #4236

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