Remove remaining threshold checks

CHANGELOG.md

@@ -35,7 +35,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Removed
 
-- Removed restriction that `threshold` has to be in (0,1) range to support logit input ([#351](https://github.com/PyTorchLightning/metrics/pull/351))
+- Removed restriction that `threshold` has to be in (0,1) range to support logit input (
+ [#351](https://github.com/PyTorchLightning/metrics/pull/351)
+ [#401](https://github.com/PyTorchLightning/metrics/pull/401))
 
 
 - Removed restriction that `preds` could not be bigger than `num_classes` to support logit input ([#357](https://github.com/PyTorchLightning/metrics/pull/357))

torchmetrics/classification/accuracy.py

@@ -139,8 +139,6 @@ class Accuracy(StatScores):
  will be used to perform the allgather
 
  Raises:
- ValueError:
- If ``threshold`` is not between ``0`` and ``1``.
  ValueError:
  If ``top_k`` is not an ``integer`` larger than ``0``.
  ValueError:
@@ -205,9 +203,6 @@ def __init__(
  self.add_state("correct", default=tensor(0), dist_reduce_fx="sum")
  self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
- if not 0 < threshold < 1:
- raise ValueError(f"The `threshold` should be a float in the (0,1) interval, got {threshold}")
-
  if top_k is not None and (not isinstance(top_k, int) or top_k <= 0):
  raise ValueError(f"The `top_k` should be an integer larger than 0, got {top_k}")
 

torchmetrics/classification/cohen_kappa.py

@@ -42,7 +42,7 @@ class labels.
  - ``target`` (long tensor): ``(N, ...)``
 
  If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
- to convert into integer labels. This is the case for binary and multi-label probabilities.
+ to convert into integer labels. This is the case for binary and multi-label probabilities or logits.
 
  If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
 
@@ -55,7 +55,8 @@ class labels.
  - ``'quadratic'``: quadratic weighting
 
  threshold:
- Threshold value for binary or multi-label probabilites. default: 0.5
+ Threshold for transforming probability or logit predictions to binary (0,1) predictions, in the case
+ of binary or multi-label inputs. Default value of 0.5 corresponds to input being probabilities.
 
  compute_on_step:
  Forward only calls ``update()`` and return None if this is set to False. default: True

torchmetrics/classification/hamming_distance.py

@@ -88,8 +88,6 @@ def __init__(
  self.add_state("correct", default=tensor(0), dist_reduce_fx="sum")
  self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
- if not 0 < threshold < 1:
- raise ValueError("The `threshold` should lie in the (0,1) interval.")
  self.threshold = threshold
 
  def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore

torchmetrics/classification/stat_scores.py

@@ -103,8 +103,6 @@ class StatScores(Metric):
  will be used to perform the allgather.
 
  Raises:
- ValueError:
- If ``threshold`` is not a ``float`` between ``0`` and ``1``.
  ValueError:
  If ``reduce`` is none of ``"micro"``, ``"macro"`` or ``"samples"``.
  ValueError:

torchmetrics/functional/classification/accuracy.py

@@ -241,8 +241,6 @@ def accuracy(
  still applies in both cases, if set.
 
  Raises:
- ValueError:
- If ``threshold`` is not a ``float`` between ``0`` and ``1``.
  ValueError:
  If ``top_k`` parameter is set for ``multi-label`` inputs.
  ValueError:
@@ -270,10 +268,6 @@ def accuracy(
  >>> accuracy(preds, target, top_k=2)
  tensor(0.6667)
  """
-
- if not 0 < threshold < 1:
- raise ValueError(f"The `threshold` should be a float in the (0,1) interval, got {threshold}")
-
  allowed_average = ["micro", "macro", "weighted", "samples", "none", None]
  if average not in allowed_average:
  raise ValueError(f"The `average` has to be one of {allowed_average}, got {average}.")

torchmetrics/utilities/checks.py

@@ -235,7 +235,7 @@ def _check_classification_inputs(
  preds: Tensor with predictions (labels or probabilities)
  target: Tensor with ground truth labels, always integers (labels)
  threshold:
- Threshold probability value for transforming probability predictions to binary
+ Threshold value for transforming probability/logit predictions to binary
  (0,1) predictions, in the case of binary or multi-label inputs.
  num_classes:
  Number of classes. If not explicitly set, the number of classes will be inferred
@@ -371,7 +371,7 @@ def _input_format_classification(
  preds: Tensor with predictions (labels or probabilities)
  target: Tensor with ground truth labels, always integers (labels)
  threshold:
- Threshold probability value for transforming probability predictions to binary
+ Threshold value for transforming probability/logit predictions to binary
  (0 or 1) predictions, in the case of binary or multi-label inputs.
  num_classes:
  Number of classes. If not explicitly set, the number of classes will be inferred