FEA Add array API support for laplacian_kernel (scikit-learn#32613)

Author: zubairshakoorarbisoft

doc/modules/array_api.rst

@@ -177,11 +177,12 @@ Metrics
 - :func:`sklearn.metrics.pairwise.cosine_distances`
 - :func:`sklearn.metrics.pairwise.pairwise_distances` (only supports "cosine", "euclidean", "manhattan" and "l2" metrics)
 - :func:`sklearn.metrics.pairwise.euclidean_distances` (see :ref:`device_support_for_float64`)
+- :func:`sklearn.metrics.pairwise.laplacian_kernel`
 - :func:`sklearn.metrics.pairwise.linear_kernel`
 - :func:`sklearn.metrics.pairwise.manhattan_distances`
 - :func:`sklearn.metrics.pairwise.paired_cosine_distances`
 - :func:`sklearn.metrics.pairwise.paired_euclidean_distances`
-- :func:`sklearn.metrics.pairwise.pairwise_kernels` (supports all `sklearn.pairwise.PAIRWISE_KERNEL_FUNCTIONS` except :func:`sklearn.metrics.pairwise.laplacian_kernel`)
+- :func:`sklearn.metrics.pairwise.pairwise_kernels`
 - :func:`sklearn.metrics.pairwise.polynomial_kernel`
 - :func:`sklearn.metrics.pairwise.rbf_kernel` (see :ref:`device_support_for_float64`)
 - :func:`sklearn.metrics.pairwise.sigmoid_kernel`

doc/whats_new/upcoming_changes/array-api/32613.feature.rst

@@ -0,0 +1,2 @@
+- :func:`sklearn.metrics.pairwise.laplacian_kernel` now supports array API compatible inputs.
+  By :user:`Zubair Shakoor <zubairshakoorarbisoft>`.

sklearn/metrics/pairwise.py

@@ -1672,7 +1672,11 @@ def laplacian_kernel(X, Y=None, gamma=None):
         gamma = 1.0 / X.shape[1]
 
     K = -gamma * manhattan_distances(X, Y)
-    np.exp(K, K)  # exponentiate K in-place
+    xp, _ = get_namespace(X, Y)
+    if _is_numpy_namespace(xp):
+        np.exp(K, K)  # exponentiate K in-place
+    else:
+        K = xp.exp(K)
     return K
 
 

sklearn/metrics/tests/test_common.py

@@ -64,6 +64,7 @@
     cosine_distances,
     cosine_similarity,
     euclidean_distances,
+    laplacian_kernel,
     linear_kernel,
     manhattan_distances,
     paired_cosine_distances,
@@ -2349,6 +2350,7 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name)
     euclidean_distances: [check_array_api_metric_pairwise],
     manhattan_distances: [check_array_api_metric_pairwise],
     linear_kernel: [check_array_api_metric_pairwise],
+    laplacian_kernel: [check_array_api_metric_pairwise],
     polynomial_kernel: [check_array_api_metric_pairwise],
     rbf_kernel: [check_array_api_metric_pairwise],
     root_mean_squared_error: [

sklearn/metrics/tests/test_pairwise.py

@@ -401,6 +401,7 @@ def test_pairwise_parallel(func, metric, kwds, dtype):
         (pairwise_distances, "manhattan", {}),
         (pairwise_kernels, "polynomial", {"degree": 1}),
         (pairwise_kernels, callable_rbf_kernel, {"gamma": 0.1}),
+        (pairwise_kernels, "laplacian", {"gamma": 0.1}),
     ],
 )
 def test_pairwise_parallel_array_api(
@@ -487,7 +488,7 @@ def test_pairwise_kernels(metric, csr_container):
 )
 @pytest.mark.parametrize(
     "metric",
-    ["rbf", "sigmoid", "polynomial", "linear", "chi2", "additive_chi2"],
+    ["rbf", "sigmoid", "polynomial", "linear", "laplacian", "chi2", "additive_chi2"],
 )
 def test_pairwise_kernels_array_api(metric, array_namespace, device, dtype_name):
     # Test array API support in pairwise_kernels.