tests: cleaning classif. (#2815)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
(cherry picked from commit dfc9e33)

src/torchmetrics/utilities/compute.py

@@ -53,6 +53,13 @@ def _safe_divide(num: Tensor, denom: Tensor, zero_division: float = 0.0) -> Tens
         denom: denominator tensor, which may contain zeros
         zero_division: value to replace elements divided by zero
 
+    Example:
+        >>> import torch
+        >>> num = torch.tensor([1.0, 2.0, 3.0])
+        >>> denom = torch.tensor([0.0, 1.0, 2.0])
+        >>> _safe_divide(num, denom)
+        tensor([0.0000, 2.0000, 1.5000])
+
     """
     num = num if num.is_floating_point() else num.float()
     denom = denom if denom.is_floating_point() else denom.float()
@@ -102,6 +109,16 @@ def _auc_compute_without_check(x: Tensor, y: Tensor, direction: float, axis: int
 
 
 def _auc_compute(x: Tensor, y: Tensor, reorder: bool = False) -> Tensor:
+    """Compute area under the curve using the trapezoidal rule.
+
+    Example:
+        >>> import torch
+        >>> x = torch.tensor([1, 2, 3, 4])
+        >>> y = torch.tensor([1, 2, 3, 4])
+        >>> _auc_compute(x, y)
+        tensor(7.5000)
+
+    """
     with torch.no_grad():
         if reorder:
             x, x_idx = torch.sort(x, stable=True)
@@ -139,7 +156,7 @@ def auc(x: Tensor, y: Tensor, reorder: bool = False) -> Tensor:
 def interp(x: Tensor, xp: Tensor, fp: Tensor) -> Tensor:
     """One-dimensional linear interpolation for monotonically increasing sample points.
 
-    Returns the one-dimensional piecewise linear interpolant to a function with
+    Returns the one-dimensional piecewise linear interpolation to a function with
     given discrete data points :math:`(xp, fp)`, evaluated at :math:`x`.
 
     Adjusted version of this https://github.com/pytorch/pytorch/issues/50334#issuecomment-1000917964
@@ -152,6 +169,13 @@ def interp(x: Tensor, xp: Tensor, fp: Tensor) -> Tensor:
     Returns:
         the interpolated values, same size as `x`.
 
+    Example:
+        >>> x = torch.tensor([0.5, 1.5, 2.5])
+        >>> xp = torch.tensor([1, 2, 3])
+        >>> fp = torch.tensor([1, 2, 3])
+        >>> interp(x, xp, fp)
+        tensor([0.5000, 1.5000, 2.5000])
+
     """
     m = _safe_divide(fp[1:] - fp[:-1], xp[1:] - xp[:-1])
     b = fp[:-1] - (m * xp[:-1])

tests/unittests/_helpers/testers.py

@@ -685,7 +685,16 @@ def inject_ignore_index(x: Tensor, ignore_index: int) -> Tensor:
 
 
 def remove_ignore_index(target: Tensor, preds: Tensor, ignore_index: Optional[int]) -> Tuple[Tensor, Tensor]:
-    """Remove samples that are equal to the ignore_index in comparison functions."""
+    """Remove samples that are equal to the ignore_index in comparison functions.
+
+    Example:
+        >>> target = torch.tensor([0, 1, 2, 3, 4])
+        >>> preds = torch.tensor([0, 1, 2, 3, 4])
+        >>> ignore_index = 2
+        >>> remove_ignore_index(target, preds, ignore_index)
+        (tensor([0, 1, 3, 4]), tensor([0, 1, 3, 4]))
+
+    """
     if ignore_index is not None:
         idx = target == ignore_index
         target, preds = deepcopy(target[~idx]), deepcopy(preds[~idx])

tests/unittests/classification/test_accuracy.py

@@ -55,14 +55,14 @@ def _reference_sklearn_accuracy_binary(preds, target, ignore_index, multidim_ave
         preds = (preds >= THRESHOLD).astype(np.uint8)
 
     if multidim_average == "global":
-        target, preds = remove_ignore_index(target, preds, ignore_index)
+        target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
         return _reference_sklearn_accuracy(target, preds)
 
     res = []
     for pred, true in zip(preds, target):
         pred = pred.flatten()
         true = true.flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         res.append(_reference_sklearn_accuracy(true, pred))
     return np.stack(res)
 
@@ -185,7 +185,7 @@ def _reference_sklearn_accuracy_multiclass(preds, target, ignore_index, multidim
     if multidim_average == "global":
         preds = preds.numpy().flatten()
         target = target.numpy().flatten()
-        target, preds = remove_ignore_index(target, preds, ignore_index)
+        target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
         if average == "micro":
             return _reference_sklearn_accuracy(target, preds)
         confmat = sk_confusion_matrix(target, preds, labels=list(range(NUM_CLASSES)))
@@ -207,7 +207,7 @@ def _reference_sklearn_accuracy_multiclass(preds, target, ignore_index, multidim
     for pred, true in zip(preds, target):
         pred = pred.flatten()
         true = true.flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         if average == "micro":
             res.append(_reference_sklearn_accuracy(true, pred))
         else:
@@ -445,13 +445,13 @@ def _reference_sklearn_accuracy_multilabel(preds, target, ignore_index, multidim
         if average == "micro":
             preds = preds.flatten()
             target = target.flatten()
-            target, preds = remove_ignore_index(target, preds, ignore_index)
+            target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
             return _reference_sklearn_accuracy(target, preds)
 
         accuracy, weights = [], []
         for i in range(preds.shape[1]):
             pred, true = preds[:, i].flatten(), target[:, i].flatten()
-            true, pred = remove_ignore_index(true, pred, ignore_index)
+            true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
             confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
             accuracy.append(_reference_sklearn_accuracy(true, pred))
             weights.append(confmat[1, 1] + confmat[1, 0])
@@ -472,15 +472,15 @@ def _reference_sklearn_accuracy_multilabel(preds, target, ignore_index, multidim
     for i in range(preds.shape[0]):
         if average == "micro":
             pred, true = preds[i].flatten(), target[i].flatten()
-            true, pred = remove_ignore_index(true, pred, ignore_index)
+            true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
             accuracy.append(_reference_sklearn_accuracy(true, pred))
             confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
             weights.append(confmat[1, 1] + confmat[1, 0])
         else:
             scores, w = [], []
             for j in range(preds.shape[1]):
                 pred, true = preds[i, j], target[i, j]
-                true, pred = remove_ignore_index(true, pred, ignore_index)
+                true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
                 scores.append(_reference_sklearn_accuracy(true, pred))
                 confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
                 w.append(confmat[1, 1] + confmat[1, 0])

tests/unittests/classification/test_auroc.py

@@ -38,7 +38,7 @@ def _reference_sklearn_auroc_binary(preds, target, max_fpr=None, ignore_index=No
     target = target.flatten().numpy()
     if not ((preds > 0) & (preds < 1)).all():
         preds = sigmoid(preds)
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     return sk_roc_auc_score(target, preds, max_fpr=max_fpr)
 
 
@@ -144,7 +144,7 @@ def _reference_sklearn_auroc_multiclass(preds, target, average="macro", ignore_i
     target = target.numpy().flatten()
     if not ((preds > 0) & (preds < 1)).all():
         preds = softmax(preds, 1)
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     return sk_roc_auc_score(target, preds, average=average, multi_class="ovr", labels=list(range(NUM_CLASSES)))
 
 

tests/unittests/classification/test_average_precision.py

@@ -47,7 +47,7 @@ def _reference_sklearn_avg_precision_binary(preds, target, ignore_index=None):
     target = target.flatten().numpy()
     if np.issubdtype(preds.dtype, np.floating) and not ((preds > 0) & (preds < 1)).all():
         preds = sigmoid(preds)
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     return sk_average_precision_score(target, preds)
 
 
@@ -156,7 +156,7 @@ def _reference_sklearn_avg_precision_multiclass(preds, target, average="macro",
     target = target.numpy().flatten()
     if not ((preds > 0) & (preds < 1)).all():
         preds = softmax(preds, 1)
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
 
     res = []
     for i in range(NUM_CLASSES):

tests/unittests/classification/test_calibration_error.py

@@ -44,7 +44,7 @@ def _reference_netcal_binary_calibration_error(preds, target, n_bins, norm, igno
     target = target.numpy().flatten()
     if not ((preds > 0) & (preds < 1)).all():
         preds = sigmoid(preds)
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     metric = ECE if norm == "l1" else MCE
     return metric(n_bins).measure(preds, target)
 
@@ -154,7 +154,7 @@ def _reference_netcal_multiclass_calibration_error(preds, target, n_bins, norm,
     if not ((preds > 0) & (preds < 1)).all():
         preds = softmax(preds, 1)
     preds = np.moveaxis(preds, 1, -1).reshape((-1, preds.shape[1]))
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     metric = ECE if norm == "l1" else MCE
     return metric(n_bins).measure(preds, target)
 

tests/unittests/classification/test_cohen_kappa.py

@@ -37,7 +37,7 @@ def _reference_sklearn_cohen_kappa_binary(preds, target, weights=None, ignore_in
         if not ((preds > 0) & (preds < 1)).all():
             preds = sigmoid(preds)
         preds = (preds >= THRESHOLD).astype(np.uint8)
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     return sk_cohen_kappa(y1=target, y2=preds, weights=weights)
 
 
@@ -136,7 +136,7 @@ def _reference_sklearn_cohen_kappa_multiclass(preds, target, weights, ignore_ind
         preds = np.argmax(preds, axis=1)
     preds = preds.flatten()
     target = target.flatten()
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     return sk_cohen_kappa(y1=target, y2=preds, weights=weights)
 
 

tests/unittests/classification/test_confusion_matrix.py

@@ -46,7 +46,7 @@ def _reference_sklearn_confusion_matrix_binary(preds, target, normalize=None, ig
         if not ((preds > 0) & (preds < 1)).all():
             preds = sigmoid(preds)
         preds = (preds >= THRESHOLD).astype(np.uint8)
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     return sk_confusion_matrix(y_true=target, y_pred=preds, labels=[0, 1], normalize=normalize)
 
 
@@ -147,7 +147,7 @@ def _reference_sklearn_confusion_matrix_multiclass(preds, target, normalize=None
         preds = np.argmax(preds, axis=1)
     preds = preds.flatten()
     target = target.flatten()
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     return sk_confusion_matrix(y_true=target, y_pred=preds, normalize=normalize, labels=list(range(NUM_CLASSES)))
 
 
@@ -298,7 +298,7 @@ def _reference_sklearn_confusion_matrix_multilabel(preds, target, normalize=None
     confmat = []
     for i in range(preds.shape[1]):
         pred, true = preds[:, i], target[:, i]
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         confmat.append(sk_confusion_matrix(true, pred, normalize=normalize, labels=[0, 1]))
     return np.stack(confmat, axis=0)
 

tests/unittests/classification/test_f_beta.py

@@ -63,14 +63,14 @@ def _reference_sklearn_fbeta_score_binary(preds, target, sk_fn, ignore_index, mu
         preds = (preds >= THRESHOLD).astype(np.uint8)
 
     if multidim_average == "global":
-        target, preds = remove_ignore_index(target, preds, ignore_index)
+        target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
         return sk_fn(target, preds, zero_division=zero_division)
 
     res = []
     for pred, true in zip(preds, target):
         pred = pred.flatten()
         true = true.flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         res.append(sk_fn(true, pred, zero_division=zero_division))
     return np.stack(res)
 
@@ -205,7 +205,7 @@ def _reference_sklearn_fbeta_score_multiclass(
     if multidim_average == "global":
         preds = preds.numpy().flatten()
         target = target.numpy().flatten()
-        target, preds = remove_ignore_index(target, preds, ignore_index)
+        target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
         return sk_fn(
             target,
             preds,
@@ -220,7 +220,7 @@ def _reference_sklearn_fbeta_score_multiclass(
     for pred, true in zip(preds, target):
         pred = pred.flatten()
         true = true.flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
 
         if len(pred) == 0 and average == "weighted":
             # The result of sk_fn([], [], labels=None, average="weighted", zero_division=zero_division)
@@ -417,13 +417,13 @@ def _reference_sklearn_fbeta_score_multilabel_global(preds, target, sk_fn, ignor
     if average == "micro":
         preds = preds.flatten()
         target = target.flatten()
-        target, preds = remove_ignore_index(target, preds, ignore_index)
+        target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
         return sk_fn(target, preds, zero_division=zero_division)
 
     fbeta_score, weights = [], []
     for i in range(preds.shape[1]):
         pred, true = preds[:, i].flatten(), target[:, i].flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         fbeta_score.append(sk_fn(true, pred, zero_division=zero_division))
         confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
         weights.append(confmat[1, 1] + confmat[1, 0])
@@ -446,15 +446,15 @@ def _reference_sklearn_fbeta_score_multilabel_local(preds, target, sk_fn, ignore
     for i in range(preds.shape[0]):
         if average == "micro":
             pred, true = preds[i].flatten(), target[i].flatten()
-            true, pred = remove_ignore_index(true, pred, ignore_index)
+            true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
             fbeta_score.append(sk_fn(true, pred, zero_division=zero_division))
             confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
             weights.append(confmat[1, 1] + confmat[1, 0])
         else:
             scores, w = [], []
             for j in range(preds.shape[1]):
                 pred, true = preds[i, j], target[i, j]
-                true, pred = remove_ignore_index(true, pred, ignore_index)
+                true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
                 scores.append(sk_fn(true, pred, zero_division=zero_division))
                 confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
                 w.append(confmat[1, 1] + confmat[1, 0])

tests/unittests/classification/test_hamming_distance.py

@@ -59,14 +59,14 @@ def _reference_sklearn_hamming_distance_binary(preds, target, ignore_index, mult
         preds = (preds >= THRESHOLD).astype(np.uint8)
 
     if multidim_average == "global":
-        target, preds = remove_ignore_index(target, preds, ignore_index)
+        target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
         return _reference_sklearn_hamming_loss(target, preds)
 
     res = []
     for pred, true in zip(preds, target):
         pred = pred.flatten()
         true = true.flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         res.append(_reference_sklearn_hamming_loss(true, pred))
     return np.stack(res)
 
@@ -167,7 +167,7 @@ def test_binary_hamming_distance_dtype_gpu(self, inputs, dtype):
 def _reference_sklearn_hamming_distance_multiclass_global(preds, target, ignore_index, average):
     preds = preds.numpy().flatten()
     target = target.numpy().flatten()
-    target, preds = remove_ignore_index(target, preds, ignore_index)
+    target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
     if average == "micro":
         return _reference_sklearn_hamming_loss(target, preds)
     confmat = sk_confusion_matrix(y_true=target, y_pred=preds, labels=list(range(NUM_CLASSES)))
@@ -191,7 +191,7 @@ def _reference_sklearn_hamming_distance_multiclass_local(preds, target, ignore_i
     for pred, true in zip(preds, target):
         pred = pred.flatten()
         true = true.flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         if average == "micro":
             res.append(_reference_sklearn_hamming_loss(true, pred))
         else:
@@ -331,13 +331,13 @@ def _reference_sklearn_hamming_distance_multilabel_global(preds, target, ignore_
     if average == "micro":
         preds = preds.flatten()
         target = target.flatten()
-        target, preds = remove_ignore_index(target, preds, ignore_index)
+        target, preds = remove_ignore_index(target=target, preds=preds, ignore_index=ignore_index)
         return _reference_sklearn_hamming_loss(target, preds)
 
     hamming, weights = [], []
     for i in range(preds.shape[1]):
         pred, true = preds[:, i].flatten(), target[:, i].flatten()
-        true, pred = remove_ignore_index(true, pred, ignore_index)
+        true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
         confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
         hamming.append(_reference_sklearn_hamming_loss(true, pred))
         weights.append(confmat[1, 1] + confmat[1, 0])
@@ -360,13 +360,13 @@ def _reference_sklearn_hamming_distance_multilabel_local(preds, target, ignore_i
     for i in range(preds.shape[0]):
         if average == "micro":
             pred, true = preds[i].flatten(), target[i].flatten()
-            true, pred = remove_ignore_index(true, pred, ignore_index)
+            true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
             hamming.append(_reference_sklearn_hamming_loss(true, pred))
         else:
             scores, w = [], []
             for j in range(preds.shape[1]):
                 pred, true = preds[i, j], target[i, j]
-                true, pred = remove_ignore_index(true, pred, ignore_index)
+                true, pred = remove_ignore_index(target=true, preds=pred, ignore_index=ignore_index)
                 scores.append(_reference_sklearn_hamming_loss(true, pred))
                 confmat = sk_confusion_matrix(true, pred, labels=[0, 1])
                 w.append(confmat[1, 1] + confmat[1, 0])