MAINT Parameters validation for sklearn.manifold.smacof (scikit-learn#26274)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

Author: ROMEEZHOU

sklearn/manifold/_mds.py

@@ -15,7 +15,7 @@
 from ..isotonic import IsotonicRegression
 from ..metrics import euclidean_distances
 from ..utils import check_array, check_random_state, check_symmetric
-from ..utils._param_validation import Hidden, Interval, StrOptions
+from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
 from ..utils.parallel import Parallel, delayed
 
 
@@ -167,6 +167,27 @@ def _smacof_single(
     return X, stress, it + 1
 
 
+@validate_params(
+    {
+        "dissimilarities": ["array-like"],
+        "metric": ["boolean"],
+        "n_components": [Interval(Integral, 1, None, closed="left")],
+        "init": ["array-like", None],
+        "n_init": [Interval(Integral, 1, None, closed="left")],
+        "n_jobs": [Integral, None],
+        "max_iter": [Interval(Integral, 1, None, closed="left")],
+        "verbose": ["verbose"],
+        "eps": [Interval(Real, 0, None, closed="left")],
+        "random_state": ["random_state"],
+        "return_n_iter": ["boolean"],
+        "normalized_stress": [
+            "boolean",
+            StrOptions({"auto"}),
+            Hidden(StrOptions({"warn"})),
+        ],
+    },
+    prefer_skip_nested_validation=True,
+)
 def smacof(
     dissimilarities,
     *,
@@ -204,7 +225,7 @@ def smacof(
 
     Parameters
     ----------
-    dissimilarities : ndarray of shape (n_samples, n_samples)
+    dissimilarities : array-like of shape (n_samples, n_samples)
         Pairwise dissimilarities between the points. Must be symmetric.
 
     metric : bool, default=True
@@ -218,7 +239,7 @@ def smacof(
         ``init`` is used to determine the dimensionality of the embedding
         space.
 
-    init : ndarray of shape (n_samples, n_components), default=None
+    init : array-like of shape (n_samples, n_components), default=None
         Starting configuration of the embedding to initialize the algorithm. By
         default, the algorithm is initialized with a randomly chosen array.
 

sklearn/tests/test_public_functions.py

@@ -202,6 +202,7 @@ def _check_function_param_validation(
     "sklearn.linear_model.orthogonal_mp_gram",
     "sklearn.linear_model.ridge_regression",
     "sklearn.metrics.accuracy_score",
+    "sklearn.manifold.smacof",
     "sklearn.metrics.auc",
     "sklearn.metrics.average_precision_score",
     "sklearn.metrics.balanced_accuracy_score",