MAINT parameter validation for metrics.pairwise.pairwise_kernels (scikit-learn#26665)

Author: marenwestermann

sklearn/metrics/pairwise.py

@@ -2279,6 +2279,19 @@ def kernel_metrics():
 }
 
 
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "Y": ["array-like", "sparse matrix", None],
+        "metric": [
+            StrOptions(set(PAIRWISE_KERNEL_FUNCTIONS) | {"precomputed"}),
+            callable,
+        ],
+        "filter_params": ["boolean"],
+        "n_jobs": [Integral, None],
+    },
+    prefer_skip_nested_validation=True,
+)
 def pairwise_kernels(
     X, Y=None, metric="linear", *, filter_params=False, n_jobs=None, **kwds
 ):
@@ -2303,18 +2316,19 @@ def pairwise_kernels(
 
     Parameters
     ----------
-    X : ndarray of shape (n_samples_X, n_samples_X) or (n_samples_X, n_features)
+    X : {array-like, sparse matrix}  of shape (n_samples_X, n_samples_X) or \
+            (n_samples_X, n_features)
         Array of pairwise kernels between samples, or a feature array.
         The shape of the array should be (n_samples_X, n_samples_X) if
         metric == "precomputed" and (n_samples_X, n_features) otherwise.
 
-    Y : ndarray of shape (n_samples_Y, n_features), default=None
+    Y : {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None
         A second feature array only if X has shape (n_samples_X, n_features).
 
     metric : str or callable, default="linear"
         The metric to use when calculating kernel between instances in a
         feature array. If metric is a string, it must be one of the metrics
-        in pairwise.PAIRWISE_KERNEL_FUNCTIONS.
+        in ``pairwise.PAIRWISE_KERNEL_FUNCTIONS``.
         If metric is "precomputed", X is assumed to be a kernel matrix.
         Alternatively, if metric is a callable function, it is called on each
         pair of instances (rows) and the resulting value recorded. The callable
@@ -2365,7 +2379,5 @@ def pairwise_kernels(
         func = PAIRWISE_KERNEL_FUNCTIONS[metric]
     elif callable(metric):
         func = partial(_pairwise_callable, metric=metric, **kwds)
-    else:
-        raise ValueError("Unknown kernel %r" % metric)
 
     return _parallel_pairwise(X, Y, func, n_jobs, **kwds)

sklearn/tests/test_public_functions.py

@@ -246,6 +246,7 @@ def _check_function_param_validation(
     "sklearn.metrics.pairwise.paired_distances",
     "sklearn.metrics.pairwise.paired_euclidean_distances",
     "sklearn.metrics.pairwise.paired_manhattan_distances",
+    "sklearn.metrics.pairwise.pairwise_kernels",
     "sklearn.metrics.pairwise.polynomial_kernel",
     "sklearn.metrics.pairwise.rbf_kernel",
     "sklearn.metrics.pairwise.sigmoid_kernel",