Fix type hints (#1472)

* Fix return type in MetricTracker.compute_all

* Fix return type in MetricTracker.best_metric

* Fix test_best_metric_for_not_well_defined_metric_collection

* Assign MetricTracker.best_metric return value to a tuple of the form (best, idx), instead of (idx, best).

* changelog

* add docstring

---------

Co-authored-by: SkafteNicki <[email protected]>
(cherry picked from commit fbdc393)

CHANGELOG.md

@@ -11,10 +11,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-- Fix compatibility between XLA in `_bincount` function ([#1471](https://github.com/Lightning-AI/metrics/pull/1471))
+- Fixed compatibility between XLA in `_bincount` function ([#1471](https://github.com/Lightning-AI/metrics/pull/1471))
 
 
--
+- Fixed type hints in methods belonging to `MetricTracker` wrapper ([#1472](https://github.com/Lightning-AI/metrics/pull/1472))
 
 
 ## [0.11.1] - 2023-01-30

docs/source/wrappers/metric_tracker.rst

@@ -14,3 +14,4 @@ ________________
 
 .. autoclass:: torchmetrics.MetricTracker
     :noindex:
+    :exclude-members: update, compute

src/torchmetrics/wrappers/tracker.py

@@ -134,8 +134,17 @@ def compute(self) -> Any:
         self._check_for_increment("compute")
         return self[-1].compute()
 
-    def compute_all(self) -> Tensor:
-        """Compute the metric value for all tracked metrics."""
+    def compute_all(self) -> Union[Tensor, Dict[str, Tensor]]:
+        """Compute the metric value for all tracked metrics.
+
+        Return:
+            Either a single tensor if the tracked base object is a single metric, else if a metric collection is
+            provide a dict of tensors will be returned
+
+        Raises:
+            ValueError:
+                If `self.increment` have not been called before this method is called.
+        """
         self._check_for_increment("compute_all")
         # The i!=0 accounts for the self._base_metric should be ignored
         res = [metric.compute() for i, metric in enumerate(self) if i != 0]
@@ -158,18 +167,32 @@ def best_metric(
     ) -> Union[
         None,
         float,
-        Tuple[int, float],
+        Tuple[float, int],
         Tuple[None, None],
         Dict[str, Union[float, None]],
-        Tuple[Dict[str, Union[int, None]], Dict[str, Union[float, None]]],
+        Tuple[Dict[str, Union[float, None]], Dict[str, Union[int, None]]],
     ]:
         """Returns the highest metric out of all tracked.
 
         Args:
             return_step: If ``True`` will also return the step with the highest metric value.
 
         Returns:
-            The best metric value, and optionally the time-step.
+            Either a single value or a tuple, depends on the value of ``return_step`` and the object being tracked.
+
+
+            - If a single metric is being tracked and ``return_step=False`` then a single tensor will be returned
+            - If a single metric is being tracked and ``return_step=True`` then a 2-element tuple will be returned,
+              where the first value is optimal value and second value is the corresponding optimal step
+            - If a metric collection is being tracked and ``return_step=False`` then a single dict will be returned,
+              where keys correspond to the different values of the collection and the values are the optimal metric
+              value
+            - If a metric collection is being bracked and ``return_step=True`` then a 2-element tuple will be returned
+              where each is a dict, with keys corresponding to the different values of th collection and the values
+              of the first dict being the optimal values and the values of the second dict being the optimal step
+
+            In addtion the value in all cases may be ``None`` if the underlying metric does have a proper defined way
+            of being optimal.
         """
         if isinstance(self._base_metric, Metric):
             fn = torch.max if self.maximize else torch.min
@@ -212,5 +235,6 @@ def best_metric(
             return value
 
     def _check_for_increment(self, method: str) -> None:
+        """Method for checking that a metric that can be updated/used for computations has been intialized."""
         if not self._increment_called:
             raise ValueError(f"`{method}` cannot be called before `.increment()` has been called")

tests/unittests/wrappers/test_tracker.py

@@ -170,7 +170,7 @@ def test_best_metric_for_not_well_defined_metric_collection(base_metric):
             assert best is None
 
     with pytest.warns(UserWarning, match="Encountered the following error when trying to get the best metric.*"):
-        idx, best = tracker.best_metric(return_step=True)
+        best, idx = tracker.best_metric(return_step=True)
 
         if isinstance(best, dict):
             assert best["MulticlassAccuracy"] is not None