Add ClassificationTask Enum

.github/ISSUE_TEMPLATE/bug_report.md

@@ -16,11 +16,14 @@ Steps to reproduce the behavior...
 
 <!-- If you have a code sample, error messages, stack traces, please provide it here as well -->
 
-#### Code sample
+<details>
+  <summary>Code sample</summary>
 
 <!-- Ideally attach a minimal code sample to reproduce the decried issue.
 Minimal means having the shortest code but still preserving the bug. -->
 
+</details>
+
 ### Expected behavior
 
 <!-- A clear and concise description of what you expected to happen. -->

.github/ISSUE_TEMPLATE/documentation.md

@@ -10,8 +10,7 @@ assignees: ''
 
 For typos and doc fixes, please go ahead and:
 
-1. Create an issue.
-1. Fix the typo.
-1. Submit a PR.
+- For a simple typo or fix, please send directly a PR (no need to create an issue)
+- If you are not sure about the proper solution, please describe here your finding...
 
 Thanks!

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,18 +2,24 @@
 
 Fixes #\<issue_number>
 
-## Before submitting
+<details>
+  <summary>Before submitting</summary>
 
-- [ ] Was this **discussed/approved** via a Github issue? (no need for typos and docs improvements)
+- [ ] Was this **discussed/agreed** via a Github issue? (no need for typos and docs improvements)
 - [ ] Did you read the [contributor guideline](https://github.com/Lightning-AI/metrics/blob/master/.github/CONTRIBUTING.md), Pull Request section?
 - [ ] Did you make sure to **update the docs**?
 - [ ] Did you write any new **necessary tests**?
 
-## PR review
+</details>
+
+<details>
+  <summary>PR review</summary>
 
 Anyone in the community is free to review the PR once the tests have passed.
 If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
 
+</details>
+
 ## Did you have fun?
 
 Make sure you had fun coding 🙃

src/torchmetrics/classification/accuracy.py

@@ -18,6 +18,7 @@
 
 from torchmetrics.functional.classification.accuracy import _accuracy_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
 
@@ -453,13 +454,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAccuracy(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassAccuracy(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAccuracy(num_labels, threshold, average, **kwargs)
         raise ValueError(

src/torchmetrics/classification/auroc.py

@@ -31,6 +31,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAUROC(BinaryPrecisionRecallCurve):
@@ -353,12 +354,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAUROC(max_fpr, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAUROC(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAUROC(num_labels, average, **kwargs)
         raise ValueError(

src/torchmetrics/classification/average_precision.py

@@ -30,6 +30,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
@@ -357,12 +358,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryAveragePrecision(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassAveragePrecision(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelAveragePrecision(num_labels, average, **kwargs)
         raise ValueError(

src/torchmetrics/classification/calibration_error.py

@@ -29,6 +29,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryCalibrationError(Metric):
@@ -268,9 +269,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"n_bins": n_bins, "norm": norm, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryCalibrationError(**kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCalibrationError(num_classes, **kwargs)
         raise ValueError(

src/torchmetrics/classification/cohen_kappa.py

@@ -23,6 +23,7 @@
     _multiclass_cohen_kappa_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryCohenKappa(BinaryConfusionMatrix):
@@ -222,9 +223,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryCohenKappa(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassCohenKappa(num_classes, **kwargs)
         raise ValueError(

src/torchmetrics/classification/confusion_matrix.py

@@ -35,6 +35,7 @@
     _multilabel_confusion_matrix_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
 from torchmetrics.utilities.plot import _PLOT_OUT_TYPE, plot_confusion_matrix
 
@@ -398,12 +399,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryConfusionMatrix(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassConfusionMatrix(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelConfusionMatrix(num_labels, threshold, **kwargs)
         raise ValueError(

src/torchmetrics/classification/exact_match.py

@@ -32,6 +32,7 @@
 )
 from torchmetrics.metric import Metric
 from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class MulticlassExactMatch(Metric):
@@ -291,10 +292,10 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassExactMatch(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelExactMatch(num_labels, threshold, **kwargs)
         raise ValueError(f"Expected argument `task` to either be `'multiclass'` or `'multilabel'` but got {task}")

src/torchmetrics/classification/f_beta.py

@@ -24,6 +24,7 @@
     _multilabel_fbeta_score_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryFBetaScore(BinaryStatScores):
@@ -729,13 +730,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryFBetaScore(beta, threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassFBetaScore(beta, num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelFBetaScore(beta, num_labels, threshold, average, **kwargs)
         raise ValueError(
@@ -780,13 +781,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryF1Score(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassF1Score(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelF1Score(num_labels, threshold, average, **kwargs)
         raise ValueError(

src/torchmetrics/classification/hamming.py

@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.hamming import _hamming_distance_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryHammingDistance(BinaryStatScores):
@@ -349,13 +350,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryHammingDistance(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassHammingDistance(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelHammingDistance(num_labels, threshold, average, **kwargs)
         raise ValueError(

src/torchmetrics/classification/hinge.py

@@ -29,6 +29,7 @@
     _multiclass_hinge_loss_update,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryHingeLoss(Metric):
@@ -253,9 +254,9 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryHingeLoss(squared, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassHingeLoss(num_classes, squared, multiclass_mode, **kwargs)
         raise ValueError(

src/torchmetrics/classification/jaccard.py

@@ -23,6 +23,7 @@
     _multilabel_jaccard_index_arg_validation,
 )
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryJaccardIndex(BinaryConfusionMatrix):
@@ -296,12 +297,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryJaccardIndex(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassJaccardIndex(num_classes, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelJaccardIndex(num_labels, threshold, average, **kwargs)
         raise ValueError(

src/torchmetrics/classification/matthews_corrcoef.py

@@ -19,6 +19,7 @@
 from torchmetrics.classification import BinaryConfusionMatrix, MulticlassConfusionMatrix, MultilabelConfusionMatrix
 from torchmetrics.functional.classification.matthews_corrcoef import _matthews_corrcoef_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryMatthewsCorrCoef(BinaryConfusionMatrix):
@@ -238,12 +239,12 @@ def __new__(
         **kwargs: Any,
     ) -> Metric:
         kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryMatthewsCorrCoef(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             return MulticlassMatthewsCorrCoef(num_classes, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelMatthewsCorrCoef(num_labels, threshold, **kwargs)
         raise ValueError(

src/torchmetrics/classification/precision_recall.py

@@ -19,6 +19,7 @@
 from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores
 from torchmetrics.functional.classification.precision_recall import _precision_recall_reduce
 from torchmetrics.metric import Metric
+from torchmetrics.utilities.enums import ClassificationTask
 
 
 class BinaryPrecision(BinaryStatScores):
@@ -620,13 +621,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryPrecision(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassPrecision(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelPrecision(num_labels, threshold, average, **kwargs)
         raise ValueError(
@@ -676,13 +677,13 @@ def __new__(
         kwargs.update(
             {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
         )
-        if task == "binary":
+        if task == ClassificationTask.BINARY:
             return BinaryRecall(threshold, **kwargs)
-        if task == "multiclass":
+        if task == ClassificationTask.MULTICLASS:
             assert isinstance(num_classes, int)
             assert isinstance(top_k, int)
             return MulticlassRecall(num_classes, top_k, average, **kwargs)
-        if task == "multilabel":
+        if task == ClassificationTask.MULTILABEL:
             assert isinstance(num_labels, int)
             return MultilabelRecall(num_labels, threshold, average, **kwargs)
         raise ValueError(