Explicit mention that 1 is the positive class in docs (#1383)

* Apply suggestions from code review

Co-authored-by: Jirka <[email protected]>
Co-authored-by: Jirka Borovec <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
(cherry picked from commit 4ecb62b)

src/torchmetrics/classification/auroc.py

@@ -46,7 +46,7 @@ class BinaryAUROC(BinaryPrecisionRecallCurve):
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/classification/average_precision.py

@@ -50,7 +50,7 @@ class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/classification/calibration_error.py

@@ -58,7 +58,7 @@ class BinaryCalibrationError(Metric):
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/classification/hinge.py

@@ -46,7 +46,7 @@ class BinaryHingeLoss(Metric):
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/classification/precision_recall_curve.py

@@ -49,7 +49,7 @@ class BinaryPrecisionRecallCurve(Metric):
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/classification/recall_at_fixed_precision.py

@@ -45,7 +45,7 @@ class BinaryRecallAtFixedPrecision(BinaryPrecisionRecallCurve):
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/classification/roc.py

@@ -42,7 +42,7 @@ class BinaryROC(BinaryPrecisionRecallCurve):
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/collections.py

@@ -11,6 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# this is just a bypass for this module name collision with build-in one
+from collections import OrderedDict
 from copy import deepcopy
 from typing import Any, Dict, Hashable, Iterable, List, Optional, Sequence, Tuple, Union
 
@@ -22,9 +24,6 @@
 from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import _flatten_dict, allclose
 
-# this is just a bypass for this module name collision with build-in one
-from torchmetrics.utilities.imports import OrderedDict
-
 
 class MetricCollection(ModuleDict):
     """MetricCollection class can be used to chain metrics that have the same call pattern into one single class.

src/torchmetrics/functional/classification/auroc.py

@@ -126,7 +126,7 @@ def binary_auroc(
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/functional/classification/average_precision.py

@@ -101,7 +101,7 @@ def binary_average_precision(
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/functional/classification/calibration_error.py

@@ -168,7 +168,7 @@ def binary_calibration_error(
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/functional/classification/hinge.py

@@ -88,7 +88,7 @@ def binary_hinge_loss(
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/functional/classification/precision_recall_curve.py

@@ -250,7 +250,7 @@ def binary_precision_recall_curve(
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/functional/classification/recall_at_fixed_precision.py

@@ -97,7 +97,7 @@ def binary_recall_at_fixed_precision(
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/functional/classification/roc.py

@@ -96,7 +96,7 @@ def binary_roc(
       observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply
       sigmoid per element.
     - ``target`` (int tensor): ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore
-      only contain {0,1} values (except if `ignore_index` is specified).
+      only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class.
 
     Additional dimension ``...`` will be flattened into the batch dimension.
 

src/torchmetrics/utilities/imports.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 """Import utilities."""
 import operator
-from collections import OrderedDict  # noqa: F401
 from functools import lru_cache
 from importlib import import_module
 from importlib.util import find_spec