Prune deprecated (#92)

* deprecation

* notes

torchmetrics/classification/checks.py

@@ -352,10 +352,11 @@ def _input_format_classification(
     ``is_multiclass=False`` (and there are up to two classes), then the data is returned as
     ``(N, X)`` binary tensors (multi-label).
 
-    Note that where a one-hot transformation needs to be performed and the number of classes
-    is not implicitly given by a ``C`` dimension, the new ``C`` dimension will either be
-    equal to ``num_classes``, if it is given, or the maximum label value in preds and
-    target.
+    Note:
+        Where a one-hot transformation needs to be performed and the number of classes
+        is not implicitly given by a ``C`` dimension, the new ``C`` dimension will either be
+        equal to ``num_classes``, if it is given, or the maximum label value in preds and
+        target.
 
     Args:
         preds: Tensor with predictions (labels or probabilities)

torchmetrics/classification/precision_recall.py

@@ -52,10 +52,8 @@ class Precision(StatScores):
             - ``'samples'``: Calculate the metric for each sample, and average the metrics
               across samples (with equal weights for each sample).
 
-            Note that what is considered a sample in the multi-dimensional multi-class case
-            depends on the value of ``mdmc_average``.
-        multilabel:
-            .. warning :: This parameter is deprecated and has no effect. Will be removed in v1.4.0.
+            .. note:: What is considered a sample in the multi-dimensional multi-class case
+                depends on the value of ``mdmc_average``.
 
         mdmc_average:
             Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
@@ -124,7 +122,6 @@ class Precision(StatScores):
         num_classes: Optional[int] = None,
         threshold: float = 0.5,
         average: str = "micro",
-        multilabel: bool = False,
         mdmc_average: Optional[str] = None,
         ignore_index: Optional[int] = None,
         top_k: Optional[int] = None,
@@ -202,10 +199,8 @@ class Recall(StatScores):
             - ``'samples'``: Calculate the metric for each sample, and average the metrics
               across samples (with equal weights for each sample).
 
-            Note that what is considered a sample in the multi-dimensional multi-class case
-            depends on the value of ``mdmc_average``.
-        multilabel:
-            .. warning :: This parameter is deprecated and has no effect. Will be removed in v1.4.0.
+            .. note:: What is considered a sample in the multi-dimensional multi-class case
+                depends on the value of ``mdmc_average``.
 
         mdmc_average:
             Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
@@ -275,7 +270,6 @@ class Recall(StatScores):
         num_classes: Optional[int] = None,
         threshold: float = 0.5,
         average: str = "micro",
-        multilabel: bool = False,
         mdmc_average: Optional[str] = None,
         ignore_index: Optional[int] = None,
         top_k: Optional[int] = None,

torchmetrics/classification/stat_scores.py

@@ -56,8 +56,8 @@ class StatScores(Metric):
             - ``'samples'``: Counts the statistics for each sample separately (over all classes).
               Each statistic is represented by a ``(N, )`` 1d tensor.
 
-            Note that what is considered a sample in the multi-dimensional multi-class case
-            depends on the value of ``mdmc_reduce``.
+            .. note:: Wwhat is considered a sample in the multi-dimensional multi-class case
+                depends on the value of ``mdmc_reduce``.
 
         num_classes:
             Number of classes. Necessary for (multi-dimensional) multi-class or multi-label data.

torchmetrics/functional/classification/precision_recall.py

@@ -76,11 +76,8 @@ def precision(
             - ``'samples'``: Calculate the metric for each sample, and average the metrics
               across samples (with equal weights for each sample).
 
-            Note that what is considered a sample in the multi-dimensional multi-class case
-            depends on the value of ``mdmc_average``.
-
-        class_reduction:
-            .. warning :: This parameter is deprecated, use ``average``. Will be removed in v1.4.0.
+            .. note:: What is considered a sample in the multi-dimensional multi-class case
+                depends on the value of ``mdmc_average``.
 
         mdmc_average:
             Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
@@ -229,8 +226,8 @@ def recall(
             - ``'samples'``: Calculate the metric for each sample, and average the metrics
               across samples (with equal weights for each sample).
 
-            Note that what is considered a sample in the multi-dimensional multi-class case
-            depends on the value of ``mdmc_average``.
+            .. note:: What is considered a sample in the multi-dimensional multi-class case
+                depends on the value of ``mdmc_average``.
 
         mdmc_average:
             Defines how averaging is done for multi-dimensional multi-class inputs (on top of the
@@ -365,8 +362,8 @@ def precision_recall(
             - ``'samples'``: Calculate the metric for each sample, and average the metrics
               across samples (with equal weights for each sample).
 
-            Note that what is considered a sample in the multi-dimensional multi-class case
-            depends on the value of ``mdmc_average``.
+            .. note:: What is considered a sample in the multi-dimensional multi-class case
+                depends on the value of ``mdmc_average``.
 
         mdmc_average:
             Defines how averaging is done for multi-dimensional multi-class inputs (on top of the

torchmetrics/functional/classification/stat_scores.py

@@ -181,8 +181,8 @@ def stat_scores(
             - ``'samples'``: Counts the statistics for each sample separately (over all classes).
               Each statistic is represented by a ``(N, )`` 1d tensor.
 
-            Note that what is considered a sample in the multi-dimensional multi-class case
-            depends on the value of ``mdmc_reduce``.
+            .. note:: What is considered a sample in the multi-dimensional multi-class case
+                depends on the value of ``mdmc_reduce``.
 
         num_classes:
             Number of classes. Necessary for (multi-dimensional) multi-class or multi-label data.