MAINT Parameters validation for lars_path_gram (scikit-learn#26380)

Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>

sklearn/linear_model/_least_angle.py

@@ -203,6 +203,24 @@ def lars_path(
  )
 
 
+@validate_params(
+ {
+ "Xy": [np.ndarray],
+ "Gram": [np.ndarray],
+ "n_samples": [Interval(Integral, 0, None, closed="left")],
+ "max_iter": [Interval(Integral, 0, None, closed="left")],
+ "alpha_min": [Interval(Real, 0, None, closed="left")],
+ "method": [StrOptions({"lar", "lasso"})],
+ "copy_X": ["boolean"],
+ "eps": [Interval(Real, 0, None, closed="neither"), None],
+ "copy_Gram": ["boolean"],
+ "verbose": ["verbose"],
+ "return_path": ["boolean"],
+ "return_n_iter": ["boolean"],
+ "positive": ["boolean"],
+ },
+ prefer_skip_nested_validation=True,
+)
 def lars_path_gram(
  Xy,
  Gram,
@@ -232,13 +250,13 @@ def lars_path_gram(
 
  Parameters
  ----------
- Xy : array-like of shape (n_features,) or (n_features, n_targets)
- Xy = np.dot(X.T, y).
+ Xy : ndarray of shape (n_features,) or (n_features, n_targets)
+ `Xy = X.T @ y`.
 
- Gram : array-like of shape (n_features, n_features)
- Gram = np.dot(X.T * X).
+ Gram : ndarray of shape (n_features, n_features)
+ `Gram = X.T @ X`.
 
- n_samples : int or float
+ n_samples : int
  Equivalent size of sample.
 
  max_iter : int, default=500
@@ -249,27 +267,27 @@ def lars_path_gram(
  regularization parameter alpha parameter in the Lasso.
 
  method : {'lar', 'lasso'}, default='lar'
- Specifies the returned model. Select ``'lar'`` for Least Angle
+ Specifies the returned model. Select `'lar'` for Least Angle
  Regression, ``'lasso'`` for the Lasso.
 
  copy_X : bool, default=True
- If ``False``, ``X`` is overwritten.
+ If `False`, `X` is overwritten.
 
  eps : float, default=np.finfo(float).eps
  The machine-precision regularization in the computation of the
  Cholesky diagonal factors. Increase this for very ill-conditioned
- systems. Unlike the ``tol`` parameter in some iterative
+ systems. Unlike the `tol` parameter in some iterative
  optimization-based algorithms, this parameter does not control
  the tolerance of the optimization.
 
  copy_Gram : bool, default=True
- If ``False``, ``Gram`` is overwritten.
+ If `False`, `Gram` is overwritten.
 
  verbose : int, default=0
  Controls output verbosity.
 
  return_path : bool, default=True
- If ``return_path==True`` returns the entire path, else returns only the
+ If `return_path==True` returns the entire path, else returns only the
  last point of the path.
 
  return_n_iter : bool, default=False
@@ -280,26 +298,26 @@ def lars_path_gram(
  This option is only allowed with method 'lasso'. Note that the model
  coefficients will not converge to the ordinary-least-squares solution
  for small values of alpha. Only coefficients up to the smallest alpha
- value (``alphas_[alphas_ > 0.].min()`` when fit_path=True) reached by
+ value (`alphas_[alphas_ > 0.].min()` when `fit_path=True`) reached by
  the stepwise Lars-Lasso algorithm are typically in congruence with the
  solution of the coordinate descent lasso_path function.
 
  Returns
  -------
- alphas : array-like of shape (n_alphas + 1,)
+ alphas : ndarray of shape (n_alphas + 1,)
  Maximum of covariances (in absolute value) at each iteration.
- ``n_alphas`` is either ``max_iter``, ``n_features`` or the
- number of nodes in the path with ``alpha >= alpha_min``, whichever
+ `n_alphas` is either `max_iter`, `n_features` or the
+ number of nodes in the path with `alpha >= alpha_min`, whichever
  is smaller.
 
- active : array-like of shape (n_alphas,)
+ active : ndarray of shape (n_alphas,)
  Indices of active variables at the end of the path.
 
- coefs : array-like of shape (n_features, n_alphas + 1)
+ coefs : ndarray of shape (n_features, n_alphas + 1)
  Coefficients along the path.
 
  n_iter : int
- Number of iterations run. Returned only if return_n_iter is set
+ Number of iterations run. Returned only if `return_n_iter` is set
  to True.
 
  See Also

sklearn/tests/test_public_functions.py

@@ -202,6 +202,7 @@ def _check_function_param_validation(
  "sklearn.isotonic.isotonic_regression",
  "sklearn.linear_model.enet_path",
  "sklearn.linear_model.lars_path",
+ "sklearn.linear_model.lars_path_gram",
  "sklearn.linear_model.lasso_path",
  "sklearn.linear_model.orthogonal_mp",
  "sklearn.linear_model.orthogonal_mp_gram",