[pre-commit.ci] pre-commit suggestions (#2484)

* [pre-commit.ci] pre-commit suggestions updates: - [github.com/crate-ci/typos: v1.16.26 → v1.20.0](crate-ci/typos@v1.16.26...v1.20.0) - [github.com/pre-commit/mirrors-prettier: v3.1.0 → v4.0.0-alpha.8](pre-commit/mirrors-prettier@v3.1.0...v4.0.0-alpha.8) - [github.com/astral-sh/ruff-pre-commit: v0.3.2 → v0.3.5](astral-sh/ruff-pre-commit@v0.3.2...v0.3.5) * Apply suggestions from code review * update & rename --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com> Co-authored-by: Jirka <jirka.borovec@seznam.cz>
Lightning-AI · Apr 10, 2024 · ca7c8eb · ca7c8eb
1 parent 8dba184
commit ca7c8eb
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -23,7 +23,7 @@ ci:
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -46,7 +46,7 @@ repos:
         exclude: pyproject.toml
 
   - repo: https://github.com/crate-ci/typos
-    rev: v1.16.26
+    rev: v1.20.7
     hooks:
       - id: typos
         # empty to do not write fixes
@@ -112,7 +112,7 @@ repos:
       - id: text-unicode-replacement-char
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.2
+    rev: v0.3.5
     hooks:
       # try to fix what is possible
       - id: ruff

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -108,7 +108,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Changed
 
 - Change default state of `SpectralAngleMapper` and `UniversalImageQualityIndex` to be tensors ([#2089](https://github.com/Lightning-AI/torchmetrics/pull/2089))
-- Use `arange` and repeat for deterministic bincount ([#2184](https://github.com/Lightning-AI/torchmetrics/pull/2184))
+- Use `torch` range func and repeat for deterministic bincount ([#2184](https://github.com/Lightning-AI/torchmetrics/pull/2184))
 
 ### Removed
 

diff --git a/src/torchmetrics/classification/cohen_kappa.py b/src/torchmetrics/classification/cohen_kappa.py
@@ -55,7 +55,7 @@ class labels.
 
     As output to ``forward`` and ``compute`` the metric returns the following output:
 
-    - ``bck`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score
+    - ``bc_kappa`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score
 
     Args:
         threshold: Threshold for transforming probability to binary (0,1) predictions

diff --git a/src/torchmetrics/classification/group_fairness.py b/src/torchmetrics/classification/group_fairness.py
@@ -177,7 +177,7 @@ class BinaryFairness(_AbstractGroupStatScores):
 
     Args:
         num_groups: The number of groups.
-        task: The task to compute. Can be either ``demographic_parity`` or ``equal_oppotunity`` or ``all``.
+        task: The task to compute. Can be either ``demographic_parity`` or ``equal_opportunity`` or ``all``.
         threshold: Threshold for transforming probability to binary {0,1} predictions.
         ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation
         validate_args: bool indicating if input arguments and tensors should be validated for correctness.

diff --git a/src/torchmetrics/detection/_mean_ap.py b/src/torchmetrics/detection/_mean_ap.py
@@ -340,8 +340,8 @@ def __init__(
         self.box_format = box_format
         self.iou_thresholds = iou_thresholds or torch.linspace(0.5, 0.95, round((0.95 - 0.5) / 0.05) + 1).tolist()
         self.rec_thresholds = rec_thresholds or torch.linspace(0.0, 1.00, round(1.00 / 0.01) + 1).tolist()
-        max_det_thr, _ = torch.sort(IntTensor(max_detection_thresholds or [1, 10, 100]))
-        self.max_detection_thresholds = max_det_thr.tolist()
+        max_det_threshold, _ = torch.sort(IntTensor(max_detection_thresholds or [1, 10, 100]))
+        self.max_detection_thresholds = max_det_threshold.tolist()
         if iou_type not in allowed_iou_types:
             raise ValueError(f"Expected argument `iou_type` to be one of {allowed_iou_types} but got {iou_type}")
         if iou_type == "segm" and not _PYCOCOTOOLS_AVAILABLE:
@@ -621,12 +621,12 @@ def _evaluate_image(
 
     @staticmethod
     def _find_best_gt_match(
-        thr: int, gt_matches: Tensor, idx_iou: float, gt_ignore: Tensor, ious: Tensor, idx_det: int
+        threshold: int, gt_matches: Tensor, idx_iou: float, gt_ignore: Tensor, ious: Tensor, idx_det: int
     ) -> int:
         """Return id of best ground truth match with current detection.
 
         Args:
-            thr:
+            threshold:
                 Current threshold value.
             gt_matches:
                 Tensor showing if a ground truth matches for threshold ``t`` exists.
@@ -645,7 +645,7 @@ def _find_best_gt_match(
         remove_mask = previously_matched | gt_ignore
         gt_ious = ious[idx_det] * ~remove_mask
         match_idx = gt_ious.argmax().item()
-        if gt_ious[match_idx] > thr:  # type: ignore[index]
+        if gt_ious[match_idx] > threshold:  # type: ignore[index]
             return match_idx  # type: ignore[return-value]
         return -1
 
@@ -679,16 +679,16 @@ def _summarize(
             prec = results["precision"]
             # IoU
             if iou_threshold is not None:
-                thr = self.iou_thresholds.index(iou_threshold)
-                prec = prec[thr, :, :, area_inds, mdet_inds]
+                threshold = self.iou_thresholds.index(iou_threshold)
+                prec = prec[threshold, :, :, area_inds, mdet_inds]
             else:
                 prec = prec[:, :, :, area_inds, mdet_inds]
         else:
             # dimension of recall: [TxKxAxM]
             prec = results["recall"]
             if iou_threshold is not None:
-                thr = self.iou_thresholds.index(iou_threshold)
-                prec = prec[thr, :, :, area_inds, mdet_inds]
+                threshold = self.iou_thresholds.index(iou_threshold)
+                prec = prec[threshold, :, :, area_inds, mdet_inds]
             else:
                 prec = prec[:, :, area_inds, mdet_inds]
 
@@ -723,26 +723,26 @@ def _calculate(self, class_ids: List) -> Tuple[MAPMetricResults, MARMetricResult
         num_rec_thrs = len(self.rec_thresholds)
         num_classes = len(class_ids)
         num_bbox_areas = len(self.bbox_area_ranges)
-        num_max_det_thrs = len(self.max_detection_thresholds)
+        num_max_det_thresholds = len(self.max_detection_thresholds)
         num_imgs = len(img_ids)
-        precision = -torch.ones((num_iou_thrs, num_rec_thrs, num_classes, num_bbox_areas, num_max_det_thrs))
-        recall = -torch.ones((num_iou_thrs, num_classes, num_bbox_areas, num_max_det_thrs))
-        scores = -torch.ones((num_iou_thrs, num_rec_thrs, num_classes, num_bbox_areas, num_max_det_thrs))
+        precision = -torch.ones((num_iou_thrs, num_rec_thrs, num_classes, num_bbox_areas, num_max_det_thresholds))
+        recall = -torch.ones((num_iou_thrs, num_classes, num_bbox_areas, num_max_det_thresholds))
+        scores = -torch.ones((num_iou_thrs, num_rec_thrs, num_classes, num_bbox_areas, num_max_det_thresholds))
 
         # move tensors if necessary
         rec_thresholds_tensor = torch.tensor(self.rec_thresholds)
 
         # retrieve E at each category, area range, and max number of detections
         for idx_cls, _ in enumerate(class_ids):
             for idx_bbox_area, _ in enumerate(self.bbox_area_ranges):
-                for idx_max_det_thrs, max_det in enumerate(self.max_detection_thresholds):
+                for idx_max_det_thresholds, max_det in enumerate(self.max_detection_thresholds):
                     recall, precision, scores = MeanAveragePrecision.__calculate_recall_precision_scores(
                         recall,
                         precision,
                         scores,
                         idx_cls=idx_cls,
                         idx_bbox_area=idx_bbox_area,
-                        idx_max_det_thrs=idx_max_det_thrs,
+                        idx_max_det_thresholds=idx_max_det_thresholds,
                         eval_imgs=eval_imgs,
                         rec_thresholds=rec_thresholds_tensor,
                         max_det=max_det,
@@ -764,26 +764,26 @@ def _summarize_results(self, precisions: Tensor, recalls: Tensor) -> Tuple[MAPMe
         """
         results = {"precision": precisions, "recall": recalls}
         map_metrics = MAPMetricResults()
-        last_max_det_thr = self.max_detection_thresholds[-1]
-        map_metrics.map = self._summarize(results, True, max_dets=last_max_det_thr)
+        last_max_det_threshold = self.max_detection_thresholds[-1]
+        map_metrics.map = self._summarize(results, True, max_dets=last_max_det_threshold)
         if 0.5 in self.iou_thresholds:
-            map_metrics.map_50 = self._summarize(results, True, iou_threshold=0.5, max_dets=last_max_det_thr)
+            map_metrics.map_50 = self._summarize(results, True, iou_threshold=0.5, max_dets=last_max_det_threshold)
         else:
             map_metrics.map_50 = torch.tensor([-1])
         if 0.75 in self.iou_thresholds:
-            map_metrics.map_75 = self._summarize(results, True, iou_threshold=0.75, max_dets=last_max_det_thr)
+            map_metrics.map_75 = self._summarize(results, True, iou_threshold=0.75, max_dets=last_max_det_threshold)
         else:
             map_metrics.map_75 = torch.tensor([-1])
-        map_metrics.map_small = self._summarize(results, True, area_range="small", max_dets=last_max_det_thr)
-        map_metrics.map_medium = self._summarize(results, True, area_range="medium", max_dets=last_max_det_thr)
-        map_metrics.map_large = self._summarize(results, True, area_range="large", max_dets=last_max_det_thr)
+        map_metrics.map_small = self._summarize(results, True, area_range="small", max_dets=last_max_det_threshold)
+        map_metrics.map_medium = self._summarize(results, True, area_range="medium", max_dets=last_max_det_threshold)
+        map_metrics.map_large = self._summarize(results, True, area_range="large", max_dets=last_max_det_threshold)
 
         mar_metrics = MARMetricResults()
         for max_det in self.max_detection_thresholds:
             mar_metrics[f"mar_{max_det}"] = self._summarize(results, False, max_dets=max_det)
-        mar_metrics.mar_small = self._summarize(results, False, area_range="small", max_dets=last_max_det_thr)
-        mar_metrics.mar_medium = self._summarize(results, False, area_range="medium", max_dets=last_max_det_thr)
-        mar_metrics.mar_large = self._summarize(results, False, area_range="large", max_dets=last_max_det_thr)
+        mar_metrics.mar_small = self._summarize(results, False, area_range="small", max_dets=last_max_det_threshold)
+        mar_metrics.mar_medium = self._summarize(results, False, area_range="medium", max_dets=last_max_det_threshold)
+        mar_metrics.mar_large = self._summarize(results, False, area_range="large", max_dets=last_max_det_threshold)
 
         return map_metrics, mar_metrics
 
@@ -794,7 +794,7 @@ def __calculate_recall_precision_scores(
         scores: Tensor,
         idx_cls: int,
         idx_bbox_area: int,
-        idx_max_det_thrs: int,
+        idx_max_det_thresholds: int,
         eval_imgs: list,
         rec_thresholds: Tensor,
         max_det: int,
@@ -838,7 +838,7 @@ def __calculate_recall_precision_scores(
             prec = torch.zeros((num_rec_thrs,))
             score = torch.zeros((num_rec_thrs,))
 
-            recall[idx, idx_cls, idx_bbox_area, idx_max_det_thrs] = rc[-1] if tp_len else 0
+            recall[idx, idx_cls, idx_bbox_area, idx_max_det_thresholds] = rc[-1] if tp_len else 0
 
             # Remove zigzags for AUC
             diff_zero = torch.zeros((1,), device=pr.device)
@@ -852,8 +852,8 @@ def __calculate_recall_precision_scores(
             inds = inds[:num_inds]  # type: ignore[misc]
             prec[:num_inds] = pr[inds]  # type: ignore[misc]
             score[:num_inds] = det_scores_sorted[inds]  # type: ignore[misc]
-            precision[idx, :, idx_cls, idx_bbox_area, idx_max_det_thrs] = prec
-            scores[idx, :, idx_cls, idx_bbox_area, idx_max_det_thrs] = score
+            precision[idx, :, idx_cls, idx_bbox_area, idx_max_det_thresholds] = prec
+            scores[idx, :, idx_cls, idx_bbox_area, idx_max_det_thresholds] = score
 
         return recall, precision, scores
 

diff --git a/src/torchmetrics/detection/mean_ap.py b/src/torchmetrics/detection/mean_ap.py
@@ -418,8 +418,8 @@ def __init__(
                 "When providing a list of max detection thresholds it should have length 3."
                 " Got value {len(max_detection_thresholds)}"
             )
-        max_det_thr, _ = torch.sort(torch.tensor(max_detection_thresholds or [1, 10, 100], dtype=torch.int))
-        self.max_detection_thresholds = max_det_thr.tolist()
+        max_det_threshold, _ = torch.sort(torch.tensor(max_detection_thresholds or [1, 10, 100], dtype=torch.int))
+        self.max_detection_thresholds = max_det_threshold.tolist()
 
         if not isinstance(class_metrics, bool):
             raise ValueError("Expected argument `class_metrics` to be a boolean")

diff --git a/src/torchmetrics/functional/classification/group_fairness.py b/src/torchmetrics/functional/classification/group_fairness.py
@@ -344,7 +344,7 @@ def binary_fairness(
         preds: Tensor with predictions.
         target: Tensor with true labels (not required for demographic_parity).
         groups: Tensor with group identifiers. The group identifiers should be ``0, 1, ..., (num_groups - 1)``.
-        task: The task to compute. Can be either ``demographic_parity`` or ``equal_oppotunity`` or ``all``.
+        task: The task to compute. Can be either ``demographic_parity`` or ``equal_opportunity`` or ``all``.
         threshold: Threshold for transforming probability to binary {0,1} predictions.
         ignore_index:
             Specifies a target value that is ignored and does not contribute to the metric calculation

diff --git a/tests/unittests/utilities/test_plot.py b/tests/unittests/utilities/test_plot.py
@@ -239,7 +239,7 @@
             partial(PearsonsContingencyCoefficient, num_classes=5),
             _nominal_input,
             _nominal_input,
-            id="pearson contigency coef",
+            id="pearson contingency coef",
         ),
         pytest.param(partial(TheilsU, num_classes=5), _nominal_input, _nominal_input, id="theils U"),
         pytest.param(partial(TschuprowsT, num_classes=5), _nominal_input, _nominal_input, id="tschuprows T"),