scikit-learn
diff --git a/‎doc/modules/array_api.rst
+1 b/‎doc/modules/array_api.rst
+1
diff --git a/‎doc/whats_new/upcoming_changes/array-api/30838.feature.rst
+2 b/‎doc/whats_new/upcoming_changes/array-api/30838.feature.rst
+2
diff --git a/‎sklearn/metrics/_classification.py
+10-11 b/‎sklearn/metrics/_classification.py
+10-11
diff --git a/‎sklearn/metrics/tests/test_common.py
+5 b/‎sklearn/metrics/tests/test_common.py
+5
@@ -136,6 +136,7 @@ Metrics
 - :func:`sklearn.metrics.explained_variance_score`
 - :func:`sklearn.metrics.f1_score`
 - :func:`sklearn.metrics.fbeta_score`
+- :func:`sklearn.metrics.hamming_loss`
 - :func:`sklearn.metrics.max_error`
 - :func:`sklearn.metrics.mean_absolute_error`
 - :func:`sklearn.metrics.mean_absolute_percentage_error`
 
@@ -0,0 +1,2 @@
+- :func:`sklearn.metrics.hamming_loss` now support Array API compatible inputs.
+  By :user:`Thomas Li <lithomas1>`
@@ -51,7 +51,6 @@
 from ..utils._unique import attach_unique
 from ..utils.extmath import _nanaverage
 from ..utils.multiclass import type_of_target, unique_labels
-from ..utils.sparsefuncs import count_nonzero
 from ..utils.validation import (
     _check_pos_label_consistency,
     _check_sample_weight,
@@ -229,12 +228,7 @@ def accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None):
     check_consistent_length(y_true, y_pred, sample_weight)
 
     if y_type.startswith("multilabel"):
-        if _is_numpy_namespace(xp):
-            differing_labels = count_nonzero(y_true - y_pred, axis=1)
-        else:
-            differing_labels = _count_nonzero(
-                y_true - y_pred, xp=xp, device=device, axis=1
-            )
+        differing_labels = _count_nonzero(y_true - y_pred, xp=xp, device=device, axis=1)
         score = xp.asarray(differing_labels == 0, device=device)
     else:
         score = y_true == y_pred
@@ -2997,15 +2991,20 @@ def hamming_loss(y_true, y_pred, *, sample_weight=None):
     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
     check_consistent_length(y_true, y_pred, sample_weight)
 
+    xp, _, device = get_namespace_and_device(y_true, y_pred, sample_weight)
+
     if sample_weight is None:
         weight_average = 1.0
     else:
-        weight_average = np.mean(sample_weight)
+        sample_weight = xp.asarray(sample_weight, device=device)
+        weight_average = _average(sample_weight, xp=xp)
 
     if y_type.startswith("multilabel"):
-        n_differences = count_nonzero(y_true - y_pred, sample_weight=sample_weight)
-        return float(
-            n_differences / (y_true.shape[0] * y_true.shape[1] * weight_average)
+        n_differences = _count_nonzero(
+            y_true - y_pred, xp=xp, device=device, sample_weight=sample_weight
+        )
+        return float(n_differences) / (
+            y_true.shape[0] * y_true.shape[1] * weight_average
         )
 
     elif y_type in ["binary", "multiclass"]:
 
@@ -2139,6 +2139,11 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name)
         check_array_api_multiclass_classification_metric,
         check_array_api_multilabel_classification_metric,
     ],
+    hamming_loss: [
+        check_array_api_binary_classification_metric,
+        check_array_api_multiclass_classification_metric,
+        check_array_api_multilabel_classification_metric,
+    ],
     mean_tweedie_deviance: [check_array_api_regression_metric],
     partial(mean_tweedie_deviance, power=-0.5): [check_array_api_regression_metric],
     partial(mean_tweedie_deviance, power=1.5): [check_array_api_regression_metric],
Original file line number	Diff line number	Diff line change
`@@ -0,0 +1,2 @@`
	`1`	+- :func:`sklearn.metrics.hamming_loss` now support Array API compatible inputs.
	`2`	+ By :user:`Thomas Li <lithomas1>`