Docs: multilabel is also a string (#2996)

Author: adamjstewart

src/torchmetrics/classification/accuracy.py

@@ -468,7 +468,7 @@ class Accuracy(_ClassificationTaskWrapper):
     Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
 
     This module is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryAccuracy`, :class:`~torchmetrics.classification.MulticlassAccuracy` and
     :class:`~torchmetrics.classification.MultilabelAccuracy` for the specific details of each argument influence and
     examples.

src/torchmetrics/classification/auroc.py

@@ -482,7 +482,7 @@ class AUROC(_ClassificationTaskWrapper):
     corresponds to random guessing.
 
     This module is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryAUROC`, :class:`~torchmetrics.classification.MulticlassAUROC` and
     :class:`~torchmetrics.classification.MultilabelAUROC` for the specific details of each argument influence and
     examples.

src/torchmetrics/classification/average_precision.py

@@ -492,7 +492,7 @@ class AveragePrecision(_ClassificationTaskWrapper):
     equivalent to the area under the precision-recall curve (AUPRC).
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryAveragePrecision`,
     :class:`~torchmetrics.classification.MulticlassAveragePrecision` and
     :class:`~torchmetrics.classification.MultilabelAveragePrecision` for the specific details of each argument

src/torchmetrics/classification/confusion_matrix.py

@@ -483,7 +483,7 @@ class ConfusionMatrix(_ClassificationTaskWrapper):
     r"""Compute the `confusion matrix`_.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryConfusionMatrix`,
     :class:`~torchmetrics.classification.MulticlassConfusionMatrix` and
     :class:`~torchmetrics.classification.MultilabelConfusionMatrix` for the specific details of each argument influence

src/torchmetrics/classification/exact_match.py

@@ -405,7 +405,7 @@ class ExactMatch(_ClassificationTaskWrapper):
     correctly classified.
 
     This module is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.MulticlassExactMatch` and
     :class:`~torchmetrics.classification.MultilabelExactMatch` for the specific details of each argument influence and
     examples.

src/torchmetrics/classification/f_beta.py

@@ -1100,7 +1100,7 @@ class FBetaScore(_ClassificationTaskWrapper):
     affected in turn.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryFBetaScore`,
     :class:`~torchmetrics.classification.MulticlassFBetaScore` and
     :class:`~torchmetrics.classification.MultilabelFBetaScore` for the specific details of each argument influence
@@ -1168,7 +1168,7 @@ class F1Score(_ClassificationTaskWrapper):
     affected in turn.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryF1Score`, :class:`~torchmetrics.classification.MulticlassF1Score` and
     :class:`~torchmetrics.classification.MultilabelF1Score` for the specific details of each argument influence and
     examples.

src/torchmetrics/classification/hamming.py

@@ -477,7 +477,7 @@ class HammingDistance(_ClassificationTaskWrapper):
     tensor.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryHammingDistance`,
     :class:`~torchmetrics.classification.MulticlassHammingDistance` and
     :class:`~torchmetrics.classification.MultilabelHammingDistance` for the specific details of each argument influence

src/torchmetrics/classification/jaccard.py

@@ -441,7 +441,7 @@ class JaccardIndex(_ClassificationTaskWrapper):
     .. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryJaccardIndex`,
     :class:`~torchmetrics.classification.MulticlassJaccardIndex` and
     :class:`~torchmetrics.classification.MultilabelJaccardIndex` for the specific details of each argument influence

src/torchmetrics/classification/logauc.py

@@ -468,7 +468,7 @@ class LogAUC(_ClassificationTaskWrapper):
     is of high importance.
 
     This module is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryLogAUC`, :class:`~torchmetrics.classification.MulticlassLogAUC` and
     :class:`~torchmetrics.classification.MultilabelLogAUC` for the specific details of each argument influence and
     examples.

src/torchmetrics/classification/matthews_corrcoef.py

@@ -374,7 +374,7 @@ class MatthewsCorrCoef(_ClassificationTaskWrapper):
     This metric measures the general correlation or quality of a classification.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryMatthewsCorrCoef`,
     :class:`~torchmetrics.classification.MulticlassMatthewsCorrCoef` and
     :class:`~torchmetrics.classification.MultilabelMatthewsCorrCoef` for the specific details of each argument influence

src/torchmetrics/classification/negative_predictive_value.py

@@ -467,7 +467,7 @@ class NegativePredictiveValue(_ClassificationTaskWrapper):
     therefore be affected in turn.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryNegativePredictiveValue`,
     :class:`~torchmetrics.classification.MulticlassNegativePredictiveValue`
     and :class:`~torchmetrics.classification.MultilabelNegativePredictiveValue` for the specific details of each

src/torchmetrics/classification/precision_fixed_recall.py

@@ -477,7 +477,7 @@ class PrecisionAtFixedRecall(_ClassificationTaskWrapper):
     a given precision level.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryPrecisionAtFixedRecall`,
     :class:`~torchmetrics.classification.MulticlassPrecisionAtFixedRecall` and
     :class:`~torchmetrics.classification.MultilabelPrecisionAtFixedRecall` for the specific details of each argument

src/torchmetrics/classification/precision_recall.py

@@ -967,7 +967,7 @@ class Precision(_ClassificationTaskWrapper):
     therefore be affected in turn.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryPrecision`, :class:`~torchmetrics.classification.MulticlassPrecision` and
     :class:`~torchmetrics.classification.MultilabelPrecision` for the specific details of each argument influence and
     examples.
@@ -1032,7 +1032,7 @@ class Recall(_ClassificationTaskWrapper):
     therefore be affected in turn.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryRecall`,
     :class:`~torchmetrics.classification.MulticlassRecall` and :class:`~torchmetrics.classification.MultilabelRecall`
     for the specific details of each argument influence and examples.

src/torchmetrics/classification/precision_recall_curve.py

@@ -630,7 +630,7 @@ class PrecisionRecallCurve(_ClassificationTaskWrapper):
     tradeoff between the two values can been seen.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryPrecisionRecallCurve`,
     :class:`~torchmetrics.classification.MulticlassPrecisionRecallCurve` and
     :class:`~torchmetrics.classification.MultilabelPrecisionRecallCurve` for the specific details of each argument

src/torchmetrics/classification/recall_fixed_precision.py

@@ -476,7 +476,7 @@ class RecallAtFixedPrecision(_ClassificationTaskWrapper):
     a given precision level.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryRecallAtFixedPrecision`,
     :class:`~torchmetrics.classification.MulticlassRecallAtFixedPrecision` and
     :class:`~torchmetrics.classification.MultilabelRecallAtFixedPrecision` for the specific details of each argument

src/torchmetrics/classification/roc.py

@@ -512,7 +512,7 @@ class ROC(_ClassificationTaskWrapper):
     different thresholds, such that the tradeoff between the two values can be seen.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryROC`,
     :class:`~torchmetrics.classification.MulticlassROC` and
     :class:`~torchmetrics.classification.MultilabelROC` for the specific details of each argument

src/torchmetrics/classification/sensitivity_specificity.py

@@ -337,7 +337,7 @@ class SensitivityAtSpecificity(_ClassificationTaskWrapper):
     find the sensitivity for a given specificity level.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinarySensitivityAtSpecificity`,
     :class:`~torchmetrics.classification.MulticlassSensitivityAtSpecificity` and
     :class:`~torchmetrics.classification.MultilabelSensitivityAtSpecificity` for the specific details of each argument

src/torchmetrics/classification/specificity.py

@@ -459,7 +459,7 @@ class Specificity(_ClassificationTaskWrapper):
     therefore be affected in turn.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinarySpecificity`, :class:`~torchmetrics.classification.MulticlassSpecificity`
     and :class:`~torchmetrics.classification.MultilabelSpecificity` for the specific details of each argument influence
     and examples.

src/torchmetrics/classification/specificity_sensitivity.py

@@ -337,7 +337,7 @@ class SpecificityAtSensitivity(_ClassificationTaskWrapper):
     find the specificity for a given sensitivity level.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinarySpecificityAtSensitivity`,
     :class:`~torchmetrics.classification.MulticlassSpecificityAtSensitivity` and
     :class:`~torchmetrics.classification.MultilabelSpecificityAtSensitivity` for the specific details of each argument

src/torchmetrics/classification/stat_scores.py

@@ -506,7 +506,7 @@ class StatScores(_ClassificationTaskWrapper):
     r"""Compute the number of true positives, false positives, true negatives, false negatives and the support.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :class:`~torchmetrics.classification.BinaryStatScores`, :class:`~torchmetrics.classification.MulticlassStatScores`
     and :class:`~torchmetrics.classification.MultilabelStatScores` for the specific details of each argument influence
     and examples.

src/torchmetrics/functional/classification/accuracy.py

@@ -394,7 +394,7 @@ def accuracy(
     Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_accuracy`,
     :func:`~torchmetrics.functional.classification.multiclass_accuracy` and
     :func:`~torchmetrics.functional.classification.multilabel_accuracy` for the specific details of

src/torchmetrics/functional/classification/auroc.py

@@ -444,7 +444,7 @@ def auroc(
     corresponds to random guessing.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_auroc`,
     :func:`~torchmetrics.functional.classification.multiclass_auroc` and
     :func:`~torchmetrics.functional.classification.multilabel_auroc` for the specific details of

src/torchmetrics/functional/classification/average_precision.py

@@ -429,7 +429,7 @@ def average_precision(
     equivalent to the area under the precision-recall curve (AUPRC).
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_average_precision`,
     :func:`~torchmetrics.functional.classification.multiclass_average_precision` and
     :func:`~torchmetrics.functional.classification.multilabel_average_precision`

src/torchmetrics/functional/classification/confusion_matrix.py

@@ -608,7 +608,7 @@ def confusion_matrix(
     r"""Compute the `confusion matrix`_.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_confusion_matrix`,
     :func:`~torchmetrics.functional.classification.multiclass_confusion_matrix` and
     :func:`~torchmetrics.functional.classification.multilabel_confusion_matrix` for

src/torchmetrics/functional/classification/f_beta.py

@@ -731,7 +731,7 @@ def fbeta_score(
         {(\beta^2 * \text{precision}) + \text{recall}}
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_fbeta_score`,
     :func:`~torchmetrics.functional.classification.multiclass_fbeta_score` and
     :func:`~torchmetrics.functional.classification.multilabel_fbeta_score` for the specific
@@ -806,7 +806,7 @@ def f1_score(
         F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}}
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_f1_score`,
     :func:`~torchmetrics.functional.classification.multiclass_f1_score` and
     :func:`~torchmetrics.functional.classification.multilabel_f1_score` for the specific

src/torchmetrics/functional/classification/hamming.py

@@ -394,7 +394,7 @@ def hamming_distance(
     tensor.
 
     This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
-    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
     :func:`~torchmetrics.functional.classification.binary_hamming_distance`,
     :func:`~torchmetrics.functional.classification.multiclass_hamming_distance` and
     :func:`~torchmetrics.functional.classification.multilabel_hamming_distance` for