MAINT Parameters validation for sklearn.linear_model.lasso_path (scikit-learn#26378)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

Author: 3 people

sklearn/linear_model/_coordinate_descent.py

@@ -170,6 +170,23 @@ def _alpha_grid(
     return np.geomspace(alpha_max, alpha_max * eps, num=n_alphas)
 
 
+@validate_params(
+    {
+        "X": ["array-like", "sparse matrix"],
+        "y": ["array-like", "sparse matrix"],
+        "eps": [Interval(Real, 0, None, closed="neither")],
+        "n_alphas": [Interval(Integral, 1, None, closed="left")],
+        "alphas": ["array-like", None],
+        "precompute": [StrOptions({"auto"}), "boolean", "array-like"],
+        "Xy": ["array-like", None],
+        "copy_X": ["boolean"],
+        "coef_init": ["array-like", None],
+        "verbose": ["verbose"],
+        "return_n_iter": ["boolean"],
+        "positive": ["boolean"],
+    },
+    prefer_skip_nested_validation=True,
+)
 def lasso_path(
     X,
     y,
@@ -224,7 +241,7 @@ def lasso_path(
     n_alphas : int, default=100
         Number of alphas along the regularization path.
 
-    alphas : ndarray, default=None
+    alphas : array-like, default=None
         List of alphas where to compute the models.
         If ``None`` alphas are set automatically.
 
@@ -242,7 +259,7 @@ def lasso_path(
     copy_X : bool, default=True
         If ``True``, X will be copied; else, it may be overwritten.
 
-    coef_init : ndarray of shape (n_features, ), default=None
+    coef_init : array-like of shape (n_features, ), default=None
         The initial values of the coefficients.
 
     verbose : bool or int, default=False

sklearn/tests/test_public_functions.py

@@ -200,6 +200,7 @@ def _check_function_param_validation(
     "sklearn.isotonic.check_increasing",
     "sklearn.isotonic.isotonic_regression",
     "sklearn.linear_model.enet_path",
+    "sklearn.linear_model.lasso_path",
     "sklearn.linear_model.orthogonal_mp",
     "sklearn.linear_model.orthogonal_mp_gram",
     "sklearn.linear_model.ridge_regression",