docs: fix example formatting (#144)

torchmetrics/classification/auroc.py

@@ -78,16 +78,15 @@ class AUROC(Metric):
         ValueError:
             If the mode of data (binary, multi-label, multi-class) changes between batches.
 
-    Example:
-        >>> # binary case
+    Example (binary case):
         >>> from torchmetrics import AUROC
         >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
         >>> target = torch.tensor([0, 0, 1, 1, 1])
         >>> auroc = AUROC(pos_label=1)
         >>> auroc(preds, target)
         tensor(0.5000)
 
-        >>> # multiclass case
+    Example (multiclass case):
         >>> preds = torch.tensor([[0.90, 0.05, 0.05],
         ...                       [0.05, 0.90, 0.05],
         ...                       [0.05, 0.05, 0.90],

torchmetrics/classification/average_precision.py

@@ -52,16 +52,15 @@ class AveragePrecision(Metric):
         process_group:
             Specify the process group on which synchronization is called. default: None (which selects the entire world)
 
-    Example:
-        >>> # binary case
+    Example (binary case):
         >>> from torchmetrics import AveragePrecision
         >>> pred = torch.tensor([0, 1, 2, 3])
         >>> target = torch.tensor([0, 1, 1, 1])
         >>> average_precision = AveragePrecision(pos_label=1)
         >>> average_precision(pred, target)
         tensor(1.)
 
-        >>> # multiclass case
+    Example (multiclass case):
         >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.75, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.05, 0.75, 0.05, 0.05],

torchmetrics/classification/hinge.py

@@ -60,8 +60,7 @@ class Hinge(Metric):
             If ``multiclass_mode`` is not: None, ``MulticlassMode.CRAMMER_SINGER``, ``"crammer-singer"``,
             ``MulticlassMode.ONE_VS_ALL`` or ``"one-vs-all"``.
 
-    Example:
-        # binary example
+    Example (binary case):
         >>> import torch
         >>> from torchmetrics import Hinge
         >>> target = torch.tensor([0, 1, 1])
@@ -70,16 +69,14 @@ class Hinge(Metric):
         >>> hinge(preds, target)
         tensor(0.3000)
 
-
-        # multiclass example, default mode
+    Example (default / multiclass case):
         >>> target = torch.tensor([0, 1, 2])
         >>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
         >>> hinge = Hinge()
         >>> hinge(preds, target)
         tensor(2.9000)
 
-
-        # multiclass example, one vs all mode
+    Example (multiclass example, one vs all mode):
         >>> target = torch.tensor([0, 1, 2])
         >>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
         >>> hinge = Hinge(multiclass_mode="one-vs-all")

torchmetrics/classification/matthews_corrcoef.py

@@ -66,7 +66,6 @@ class MatthewsCorrcoef(Metric):
             will be used to perform the allgather
 
     Example:
-
         >>> from torchmetrics import MatthewsCorrcoef
         >>> target = torch.tensor([1, 1, 0, 0])
         >>> preds = torch.tensor([0, 1, 0, 0])

torchmetrics/classification/precision_recall_curve.py

@@ -52,8 +52,7 @@ class PrecisionRecallCurve(Metric):
         process_group:
             Specify the process group on which synchronization is called. default: None (which selects the entire world)
 
-    Example:
-        >>> # binary case
+    Example (binary case):
         >>> from torchmetrics import PrecisionRecallCurve
         >>> pred = torch.tensor([0, 1, 2, 3])
         >>> target = torch.tensor([0, 1, 1, 0])
@@ -66,7 +65,7 @@ class PrecisionRecallCurve(Metric):
         >>> thresholds
         tensor([1, 2, 3])
 
-        >>> # multiclass case
+    Example (multiclass case):
         >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.75, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.05, 0.75, 0.05, 0.05],

torchmetrics/classification/roc.py

@@ -53,7 +53,6 @@ class ROC(Metric):
             will be used to perform the allgather
 
     Example (binary case):
-
         >>> from torchmetrics import ROC
         >>> pred = torch.tensor([0, 1, 2, 3])
         >>> target = torch.tensor([0, 1, 1, 1])
@@ -67,8 +66,6 @@ class ROC(Metric):
         tensor([4, 3, 2, 1, 0])
 
     Example (multiclass case):
-
-        >>> from torchmetrics import ROC
         >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.75, 0.05, 0.05],
         ...                      [0.05, 0.05, 0.75, 0.05],
@@ -87,8 +84,6 @@ class ROC(Metric):
          tensor([1.7500, 0.7500, 0.0500])]
 
     Example (multilabel case):
-
-        >>> from torchmetrics import ROC
         >>> pred = torch.tensor([[0.8191, 0.3680, 0.1138],
         ...                      [0.3584, 0.7576, 0.1183],
         ...                      [0.2286, 0.3468, 0.1338],

torchmetrics/collections.py

@@ -46,8 +46,7 @@ class MetricCollection(nn.ModuleDict):
         ValueError:
             If ``metrics`` is not a ``list``, ``tuple`` or a ``dict``.
 
-    Example:
-        >>> # input as list
+    Example (input as list):
         >>> import torch
         >>> from pprint import pprint
         >>> from torchmetrics import MetricCollection, Accuracy, Precision, Recall
@@ -59,7 +58,7 @@ class MetricCollection(nn.ModuleDict):
         >>> metrics(preds, target)
         {'Accuracy': tensor(0.1250), 'Precision': tensor(0.0667), 'Recall': tensor(0.1111)}
 
-        >>> # input as dict
+    Example (input as dict):
         >>> metrics = MetricCollection({'micro_recall': Recall(num_classes=3, average='micro'),
         ...                             'macro_recall': Recall(num_classes=3, average='macro')})
         >>> same_metric = metrics.clone()

torchmetrics/functional/classification/auroc.py

@@ -177,15 +177,14 @@ def auroc(
         ValueError:
             If ``average`` is none of ``None``, ``"macro"`` or ``"weighted"``.
 
-    Example:
-        >>> # binary case
+    Example (binary case):
         >>> from torchmetrics.functional import auroc
         >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
         >>> target = torch.tensor([0, 0, 1, 1, 1])
         >>> auroc(preds, target, pos_label=1)
         tensor(0.5000)
 
-        >>> # multiclass case
+    Example (multiclass case):
         >>> preds = torch.tensor([[0.90, 0.05, 0.05],
         ...                       [0.05, 0.90, 0.05],
         ...                       [0.05, 0.05, 0.90],

torchmetrics/functional/classification/average_precision.py

@@ -77,15 +77,14 @@ def average_precision(
         tensor with average precision. If multiclass will return list
         of such tensors, one for each class
 
-    Example:
-        >>> # binary case
+    Example (binary case):
         >>> from torchmetrics.functional import average_precision
         >>> pred = torch.tensor([0, 1, 2, 3])
         >>> target = torch.tensor([0, 1, 1, 1])
         >>> average_precision(pred, target, pos_label=1)
         tensor(1.)
 
-        >>> # multiclass case
+    Example (multiclass case):
         >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.75, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.05, 0.75, 0.05, 0.05],

torchmetrics/functional/classification/hinge.py

@@ -154,24 +154,21 @@ def hinge(
             If ``multiclass_mode`` is not: None, ``MulticlassMode.CRAMMER_SINGER``, ``"crammer-singer"``,
             ``MulticlassMode.ONE_VS_ALL`` or ``"one-vs-all"``.
 
-    Example:
-        # binary example
+    Example (binary case):
         >>> import torch
         >>> from torchmetrics.functional import hinge
         >>> target = torch.tensor([0, 1, 1])
         >>> preds = torch.tensor([-2.2, 2.4, 0.1])
         >>> hinge(preds, target)
         tensor(0.3000)
 
-
-        # multiclass example, default mode
+    Example (default / multiclass case):
         >>> target = torch.tensor([0, 1, 2])
         >>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
         >>> hinge(preds, target)
         tensor(2.9000)
 
-
-        # multiclass example, one vs all mode
+    Example (multiclass example, one vs all mode):
         >>> target = torch.tensor([0, 1, 2])
         >>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
         >>> hinge(preds, target, multiclass_mode="one-vs-all")

torchmetrics/functional/classification/precision_recall_curve.py

@@ -203,8 +203,7 @@ def precision_recall_curve(
             If the number of classes deduced from ``preds`` is not the same as the
             ``num_classes`` provided.
 
-    Example:
-        >>> # binary case
+    Example (binary case):
         >>> from torchmetrics.functional import precision_recall_curve
         >>> pred = torch.tensor([0, 1, 2, 3])
         >>> target = torch.tensor([0, 1, 1, 0])
@@ -216,7 +215,7 @@ def precision_recall_curve(
         >>> thresholds
         tensor([1, 2, 3])
 
-        >>> # multiclass case
+    Example (multiclass case):
         >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.75, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.05, 0.75, 0.05, 0.05],

torchmetrics/functional/classification/roc.py

@@ -121,7 +121,6 @@ def roc(
             If multiclass or multilabel, this is a list of such tensors, one for each class/label.
 
     Example (binary case):
-
         >>> from torchmetrics.functional import roc
         >>> pred = torch.tensor([0, 1, 2, 3])
         >>> target = torch.tensor([0, 1, 1, 1])
@@ -134,7 +133,6 @@ def roc(
         tensor([4, 3, 2, 1, 0])
 
     Example (multiclass case):
-
         >>> from torchmetrics.functional import roc
         >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05],
         ...                      [0.05, 0.75, 0.05, 0.05],
@@ -153,7 +151,6 @@ def roc(
          tensor([1.7500, 0.7500, 0.0500])]
 
     Example (multilabel case):
-
         >>> from torchmetrics.functional import roc
         >>> pred = torch.tensor([[0.8191, 0.3680, 0.1138],
         ...                      [0.3584, 0.7576, 0.1183],

torchmetrics/functional/retrieval/average_precision.py

@@ -34,6 +34,7 @@ def retrieval_average_precision(preds: Tensor, target: Tensor) -> Tensor:
         a single-value tensor with the average precision (AP) of the predictions ``preds`` w.r.t. the labels ``target``.
 
     Example:
+        >>> from torchmetrics.functional import retrieval_average_precision
         >>> preds = tensor([0.2, 0.3, 0.5])
         >>> target = tensor([True, False, True])
         >>> retrieval_average_precision(preds, target)

torchmetrics/functional/retrieval/reciprocal_rank.py

@@ -34,6 +34,7 @@ def retrieval_reciprocal_rank(preds: Tensor, target: Tensor) -> Tensor:
         a single-value tensor with the reciprocal rank (RR) of the predictions ``preds`` wrt the labels ``target``.
 
     Example:
+        >>> from torchmetrics.functional import retrieval_reciprocal_rank
         >>> preds = torch.tensor([0.2, 0.3, 0.5])
         >>> target = torch.tensor([False, True, False])
         >>> retrieval_reciprocal_rank(preds, target)

torchmetrics/utilities/data.py

@@ -242,7 +242,6 @@ def get_group_indexes(idx: Tensor) -> List[Tensor]:
         A list of integer `torch.Tensor`s
 
     Example:
-
         >>> indexes = torch.tensor([0, 0, 0, 1, 1, 1, 1])
         >>> groups = get_group_indexes(indexes)
         >>> groups