API Deprecate n_alphas in LinearModelCV in favor of alphas (scikit-learn#30616)

Co-authored-by: Jérémie du Boisberranger <jeremie@probabl.ai>

Author: KANNAHWORLD

doc/whats_new/upcoming_changes/sklearn.linear_model/30616.api.rst

@@ -0,0 +1,9 @@
+The parameter `n_alphas` has been deprecated in the following classes:
+:class:`linear_model.ElasticNetCV` and :class:`linear_model.LassoCV`
+and :class:`linear_model.MultiTaskElasticNetCV` 
+and :class:`linear_model.MultiTaskLassoCV`, and will be removed in 1.9. The parameter
+`alphas` now supports both integers and array-likes, removing the need for `n_alphas`.
+From now on, only `alphas` should be set to either indicate the number of alphas to
+automatically generate (int) or to provide a list of alphas (array-like) to test along
+the regularization path.
+By :user:`Siddharth Bansal <KANNAHWORLD >`.

sklearn/linear_model/_coordinate_descent.py

@@ -23,7 +23,7 @@
     _raise_for_params,
     get_routing_for_object,
 )
-from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
 from ..utils.extmath import safe_sparse_dot
 from ..utils.metadata_routing import (
     _routing_enabled,
@@ -1493,8 +1493,17 @@ class LinearModelCV(MultiOutputMixin, LinearModel, ABC):
 
     _parameter_constraints: dict = {
         "eps": [Interval(Real, 0, None, closed="neither")],
-        "n_alphas": [Interval(Integral, 1, None, closed="left")],
-        "alphas": ["array-like", None],
+        "n_alphas": [
+            Interval(Integral, 1, None, closed="left"),
+            Hidden(StrOptions({"deprecated"})),
+        ],
+        # TODO(1.9): remove "warn" and None options.
+        "alphas": [
+            Interval(Integral, 1, None, closed="left"),
+            "array-like",
+            None,
+            Hidden(StrOptions({"warn"})),
+        ],
         "fit_intercept": ["boolean"],
         "precompute": [StrOptions({"auto"}), "array-like", "boolean"],
         "max_iter": [Interval(Integral, 1, None, closed="left")],
@@ -1512,8 +1521,8 @@ class LinearModelCV(MultiOutputMixin, LinearModel, ABC):
     def __init__(
         self,
         eps=1e-3,
-        n_alphas=100,
-        alphas=None,
+        n_alphas="deprecated",
+        alphas="warn",
         fit_intercept=True,
         precompute="auto",
         max_iter=1000,
@@ -1595,6 +1604,40 @@ def fit(self, X, y, sample_weight=None, **params):
         """
         _raise_for_params(params, self, "fit")
 
+        # TODO(1.9): remove n_alphas and alphas={"warn", None}; set alphas=100 by
+        # default. Remove these deprecations messages and use self.alphas directly
+        # instead of self._alphas.
+        if self.n_alphas == "deprecated":
+            self._alphas = 100
+        else:
+            warnings.warn(
+                "'n_alphas' was deprecated in 1.7 and will be removed in 1.9. "
+                "'alphas' now accepts an integer value which removes the need to pass "
+                "'n_alphas'. The default value of 'alphas' will change from None to "
+                "100 in 1.9. Pass an explicit value to 'alphas' and leave 'n_alphas' "
+                "to its default value to silence this warning.",
+                FutureWarning,
+            )
+            self._alphas = self.n_alphas
+
+        if isinstance(self.alphas, str) and self.alphas == "warn":
+            # - If self.n_alphas == "deprecated", both are left to their default values
+            #   so we don't warn since the future default behavior will be the same as
+            #   the current default behavior.
+            # - If self.n_alphas != "deprecated", then we already warned about it
+            #   and the warning message mentions the future self.alphas default, so
+            #   no need to warn a second time.
+            pass
+        elif self.alphas is None:
+            warnings.warn(
+                "'alphas=None' is deprecated and will be removed in 1.9, at which "
+                "point the default value will be set to 100. Set 'alphas=100' "
+                "to silence this warning.",
+                FutureWarning,
+            )
+        else:
+            self._alphas = self.alphas
+
         # This makes sure that there is no duplication in memory.
         # Dealing right with copy_X is important in the following:
         # Multiple functions touch X and subsamples of X and can induce a
@@ -1692,7 +1735,6 @@ def fit(self, X, y, sample_weight=None, **params):
         path_params.pop("cv", None)
         path_params.pop("n_jobs", None)
 
-        alphas = self.alphas
         n_l1_ratio = len(l1_ratios)
 
         check_scalar_alpha = partial(
@@ -1702,26 +1744,26 @@ def fit(self, X, y, sample_weight=None, **params):
             include_boundaries="left",
         )
 
-        if alphas is None:
+        if isinstance(self._alphas, Integral):
             alphas = [
                 _alpha_grid(
                     X,
                     y,
                     l1_ratio=l1_ratio,
                     fit_intercept=self.fit_intercept,
                     eps=self.eps,
-                    n_alphas=self.n_alphas,
+                    n_alphas=self._alphas,
                     copy_X=self.copy_X,
                     sample_weight=sample_weight,
                 )
                 for l1_ratio in l1_ratios
             ]
         else:
             # Making sure alphas entries are scalars.
-            for index, alpha in enumerate(alphas):
+            for index, alpha in enumerate(self._alphas):
                 check_scalar_alpha(alpha, f"alphas[{index}]")
             # Making sure alphas is properly ordered.
-            alphas = np.tile(np.sort(alphas)[::-1], (n_l1_ratio, 1))
+            alphas = np.tile(np.sort(self._alphas)[::-1], (n_l1_ratio, 1))
 
         # We want n_alphas to be the number of alphas used for each l1_ratio.
         n_alphas = len(alphas[0])
@@ -1807,7 +1849,7 @@ def fit(self, X, y, sample_weight=None, **params):
 
         self.l1_ratio_ = best_l1_ratio
         self.alpha_ = best_alpha
-        if self.alphas is None:
+        if isinstance(self._alphas, Integral):
             self.alphas_ = np.asarray(alphas)
             if n_l1_ratio == 1:
                 self.alphas_ = self.alphas_[0]
@@ -1897,9 +1939,22 @@ class LassoCV(RegressorMixin, LinearModelCV):
     n_alphas : int, default=100
         Number of alphas along the regularization path.
 
-    alphas : array-like, default=None
-        List of alphas where to compute the models.
-        If ``None`` alphas are set automatically.
+        .. deprecated:: 1.7
+            `n_alphas` was deprecated in 1.7 and will be removed in 1.9. Use `alphas`
+            instead.
+
+    alphas : array-like or int, default=None
+        Values of alphas to test along the regularization path.
+        If int, `alphas` values are generated automatically.
+        If array-like, list of alpha values to use.
+
+        .. versionchanged:: 1.7
+            `alphas` accepts an integer value which removes the need to pass
+            `n_alphas`.
+
+        .. deprecated:: 1.7
+            `alphas=None` was deprecated in 1.7 and will be removed in 1.9, at which
+            point the default value will be set to 100.
 
     fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
@@ -2049,8 +2104,8 @@ def __init__(
         self,
         *,
         eps=1e-3,
-        n_alphas=100,
-        alphas=None,
+        n_alphas="deprecated",
+        alphas="warn",
         fit_intercept=True,
         precompute="auto",
         max_iter=1000,
@@ -2155,9 +2210,22 @@ class ElasticNetCV(RegressorMixin, LinearModelCV):
     n_alphas : int, default=100
         Number of alphas along the regularization path, used for each l1_ratio.
 
-    alphas : array-like, default=None
-        List of alphas where to compute the models.
-        If None alphas are set automatically.
+        .. deprecated:: 1.7
+            `n_alphas` was deprecated in 1.7 and will be removed in 1.9. Use `alphas`
+            instead.
+
+    alphas : array-like or int, default=None
+        Values of alphas to test along the regularization path, used for each l1_ratio.
+        If int, `alphas` values are generated automatically.
+        If array-like, list of alpha values to use.
+
+        .. versionchanged:: 1.7
+            `alphas` accepts an integer value which removes the need to pass
+            `n_alphas`.
+
+        .. deprecated:: 1.7
+            `alphas=None` was deprecated in 1.7 and will be removed in 1.9, at which
+            point the default value will be set to 100.
 
     fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
@@ -2326,8 +2394,8 @@ def __init__(
         *,
         l1_ratio=0.5,
         eps=1e-3,
-        n_alphas=100,
-        alphas=None,
+        n_alphas="deprecated",
+        alphas="warn",
         fit_intercept=True,
         precompute="auto",
         max_iter=1000,
@@ -2845,9 +2913,22 @@ class MultiTaskElasticNetCV(RegressorMixin, LinearModelCV):
     n_alphas : int, default=100
         Number of alphas along the regularization path.
 
-    alphas : array-like, default=None
-        List of alphas where to compute the models.
-        If not provided, set automatically.
+        .. deprecated:: 1.7
+            `n_alphas` was deprecated in 1.7 and will be removed in 1.9. Use `alphas`
+            instead.
+
+    alphas : array-like or int, default=None
+        Values of alphas to test along the regularization path, used for each l1_ratio.
+        If int, `alphas` values are generated automatically.
+        If array-like, list of alpha values to use.
+
+        .. versionchanged:: 1.7
+            `alphas` accepts an integer value which removes the need to pass
+            `n_alphas`.
+
+        .. deprecated:: 1.7
+            `alphas=None` was deprecated in 1.7 and will be removed in 1.9, at which
+            point the default value will be set to 100.
 
     fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
@@ -2991,8 +3072,8 @@ def __init__(
         *,
         l1_ratio=0.5,
         eps=1e-3,
-        n_alphas=100,
-        alphas=None,
+        n_alphas="deprecated",
+        alphas="warn",
         fit_intercept=True,
         max_iter=1000,
         tol=1e-4,
@@ -3088,9 +3169,22 @@ class MultiTaskLassoCV(RegressorMixin, LinearModelCV):
     n_alphas : int, default=100
         Number of alphas along the regularization path.
 
-    alphas : array-like, default=None
-        List of alphas where to compute the models.
-        If not provided, set automatically.
+        .. deprecated:: 1.7
+            `n_alphas` was deprecated in 1.7 and will be removed in 1.9. Use `alphas`
+            instead.
+
+    alphas : array-like or int, default=None
+        Values of alphas to test along the regularization path.
+        If int, `alphas` values are generated automatically.
+        If array-like, list of alpha values to use.
+
+        .. versionchanged:: 1.7
+            `alphas` accepts an integer value which removes the need to pass
+            `n_alphas`.
+
+        .. deprecated:: 1.7
+            `alphas=None` was deprecated in 1.7 and will be removed in 1.9, at which
+            point the default value will be set to 100.
 
     fit_intercept : bool, default=True
         Whether to calculate the intercept for this model. If set
@@ -3230,8 +3324,8 @@ def __init__(
         self,
         *,
         eps=1e-3,
-        n_alphas=100,
-        alphas=None,
+        n_alphas="deprecated",
+        alphas="warn",
         fit_intercept=True,
         max_iter=1000,
         tol=1e-4,