From 8d4b501072a966878f6c144273633b7559d10d42 Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Sun, 3 Apr 2022 22:11:49 -0400
Subject: [PATCH 01/33] Update _alpha_grid to take sample_weight

It seems like this single call to _preprocess_data suffices in all cases.
---
 sklearn/linear_model/_coordinate_descent.py | 64 +++++++++------------
 1 file changed, 28 insertions(+), 36 deletions(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index b450790da5a07..bc338ebaa41db 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -92,6 +92,7 @@ def _alpha_grid(
     n_alphas=100,
     normalize=False,
     copy_X=True,
+    sample_weight=None,
 ):
     """Compute the grid of alpha values for elastic net parameter search
 
@@ -138,6 +139,8 @@ def _alpha_grid(
 
     copy_X : bool, default=True
         If ``True``, X will be copied; else, it may be overwritten.
+
+    sample_weight : ndarray of shape (n_samples,)
     """
     if l1_ratio == 0:
         raise ValueError(
@@ -146,47 +149,35 @@ def _alpha_grid(
             "your estimator with the appropriate `alphas=` "
             "argument."
         )
-    n_samples = len(y)
-
-    sparse_center = False
-    if Xy is None:
-        X_sparse = sparse.isspmatrix(X)
-        sparse_center = X_sparse and (fit_intercept or normalize)
-        X = check_array(
-            X, accept_sparse="csc", copy=(copy_X and fit_intercept and not X_sparse)
+    if Xy is not None and ((sample_weight is None) or (fit_intercept is False)):
+        # In this case, the precomputed Xy should be valid.
+        pass
+    else:
+        # Compute Xy.
+        X, y, X_offset, y_offset, X_scale = _preprocess_data(
+            X,
+            y,
+            fit_intercept,
+            normalize=normalize,
+            copy=copy_X,
+            sample_weight=sample_weight,
+            check_input=False,
         )
-        if not X_sparse:
-            # X can be touched inplace thanks to the above line
-            X, y, _, _, _ = _preprocess_data(X, y, fit_intercept, normalize, copy=False)
-        Xy = safe_sparse_dot(X.T, y, dense_output=True)
-
-        if sparse_center:
-            # Workaround to find alpha_max for sparse matrices.
-            # since we should not destroy the sparsity of such matrices.
-            _, _, X_offset, _, X_scale = _preprocess_data(
-                X, y, fit_intercept, normalize
-            )
-            mean_dot = X_offset * np.sum(y)
+        if sample_weight is not None:
+            yw = y * sample_weight / sample_weight.mean()
+        else:
+            yw = y
+        if sparse.issparse(X):
+            Xy = safe_sparse_dot(X.T, yw, dense_output=True) - np.sum(yw) * X_offset
+        else:
+            Xy = np.dot(X.T, yw)
 
     if Xy.ndim == 1:
         Xy = Xy[:, np.newaxis]
-
-    if sparse_center:
-        if fit_intercept:
-            Xy -= mean_dot[:, np.newaxis]
-        if normalize:
-            Xy /= X_scale[:, np.newaxis]
-
-    alpha_max = np.sqrt(np.sum(Xy**2, axis=1)).max() / (n_samples * l1_ratio)
-
+    alpha_max = np.sqrt(np.sum(Xy**2, axis=1)).max() / (l1_ratio * X.shape[0])
     if alpha_max <= np.finfo(float).resolution:
-        alphas = np.empty(n_alphas)
-        alphas.fill(np.finfo(float).resolution)
-        return alphas
-
-    return np.logspace(np.log10(alpha_max * eps), np.log10(alpha_max), num=n_alphas)[
-        ::-1
-    ]
+        return np.full(n_alphas, np.finfo(float).resolution)
+    return np.logspace(np.log10(alpha_max), np.log10(alpha_max * eps), num=n_alphas)
 
 
 def lasso_path(
@@ -1660,6 +1651,7 @@ def fit(self, X, y, sample_weight=None):
                     n_alphas=self.n_alphas,
                     normalize=_normalize,
                     copy_X=self.copy_X,
+                    sample_weight=sample_weight,
                 )
                 for l1_ratio in l1_ratios
             ]

From 2f494db40f647ca99f57807a3934943caecc7d58 Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Sun, 3 Apr 2022 22:12:51 -0400
Subject: [PATCH 02/33] Add a simple test for alpha_max with sample_weight

This tiny example was given in https://github.com/scikit-learn/scikit-learn/issues/22914.
The test merely asserts that alpha_max is large enough to force the coefficient to 0.
---
 .../linear_model/tests/test_coordinate_descent.py    | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index e5d7ba358c1f5..f38fd3933371b 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1642,6 +1642,18 @@ def test_enet_cv_grid_search(sample_weight):
     assert reg.alpha_ == pytest.approx(gs.best_params_["alpha"])
 
 
+@pytest.mark.parametrize("sparseX", [False, True])
+def test_enet_alpha_max_sample_weight(sparseX):
+    X = np.array([[3, 1], [2, 5], [5, 3], [1, 4]])
+    beta = np.array([1, 1])
+    y = X @ beta
+    sample_weight = np.array([10, 1, 10, 1])
+    if sparseX:
+        X = sparse.csc_matrix(X)
+    reg = ElasticNetCV(n_alphas=1, cv=2).fit(X, y, sample_weight=sample_weight)
+    assert_almost_equal(reg.coef_, 0)
+
+
 @pytest.mark.parametrize("fit_intercept", [True, False])
 @pytest.mark.parametrize("l1_ratio", [0, 0.5, 1])
 @pytest.mark.parametrize("precompute", [False, True])

From fa2c8215bf61e1f553dcfed3dae539a3769a3b00 Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Mon, 4 Apr 2022 09:06:16 -0400
Subject: [PATCH 03/33] Update test

As per reviewer's suggestions:
(1) Clarify eps=1.
(2) Parameterize `fit_intercept`.
---
 sklearn/linear_model/tests/test_coordinate_descent.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index f38fd3933371b..807429985dcac 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1643,14 +1643,16 @@ def test_enet_cv_grid_search(sample_weight):
 
 
 @pytest.mark.parametrize("sparseX", [False, True])
-def test_enet_alpha_max_sample_weight(sparseX):
+@pytest.mark.parametrize("fit_intercept", [False, True])
+def test_enet_alpha_max_sample_weight(sparseX, fit_intercept):
     X = np.array([[3, 1], [2, 5], [5, 3], [1, 4]])
     beta = np.array([1, 1])
     y = X @ beta
     sample_weight = np.array([10, 1, 10, 1])
     if sparseX:
         X = sparse.csc_matrix(X)
-    reg = ElasticNetCV(n_alphas=1, cv=2).fit(X, y, sample_weight=sample_weight)
+    reg = ElasticNetCV(n_alphas=1, cv=2, eps=1, fit_intercept=fit_intercept)
+    reg.fit(X, y, sample_weight=sample_weight)
     assert_almost_equal(reg.coef_, 0)
 
 

From 75e65842e9312af3296a96ccab7d38dfbb0b59e6 Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Mon, 4 Apr 2022 09:10:45 -0400
Subject: [PATCH 04/33] Clarify _alpha_grid.

(1) Give the name `n_samples` to the quantity `X.shape[0]`.
(2) Clarify that `y_offset` and `X_scale` are not used, since these are already applied to the data by `_preprocess_data`.
---
 sklearn/linear_model/_coordinate_descent.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index bc338ebaa41db..6c710af8b5137 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -154,7 +154,7 @@ def _alpha_grid(
         pass
     else:
         # Compute Xy.
-        X, y, X_offset, y_offset, X_scale = _preprocess_data(
+        X, y, X_offset, _, _ = _preprocess_data(
             X,
             y,
             fit_intercept,
@@ -174,7 +174,9 @@ def _alpha_grid(
 
     if Xy.ndim == 1:
         Xy = Xy[:, np.newaxis]
-    alpha_max = np.sqrt(np.sum(Xy**2, axis=1)).max() / (l1_ratio * X.shape[0])
+    n_samples = X.shape[0]
+    alpha_max = np.max(np.sqrt(np.sum(Xy**2, axis=1))) / (l1_ratio * n_samples)
+
     if alpha_max <= np.finfo(float).resolution:
         return np.full(n_alphas, np.finfo(float).resolution)
     return np.logspace(np.log10(alpha_max), np.log10(alpha_max * eps), num=n_alphas)

From 8b6cfc0bfb53faf56cd6e410e35b4b83ebc3f219 Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Tue, 5 Apr 2022 18:16:49 -0400
Subject: [PATCH 05/33] Clarify notation

---
 sklearn/linear_model/_coordinate_descent.py | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index 6c710af8b5137..3655bd667c2d0 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -150,10 +150,8 @@ def _alpha_grid(
             "argument."
         )
     if Xy is not None and ((sample_weight is None) or (fit_intercept is False)):
-        # In this case, the precomputed Xy should be valid.
-        pass
+        Xyw = Xy
     else:
-        # Compute Xy.
         X, y, X_offset, _, _ = _preprocess_data(
             X,
             y,
@@ -168,14 +166,14 @@ def _alpha_grid(
         else:
             yw = y
         if sparse.issparse(X):
-            Xy = safe_sparse_dot(X.T, yw, dense_output=True) - np.sum(yw) * X_offset
+            Xyw = safe_sparse_dot(X.T, yw, dense_output=True) - np.sum(yw) * X_offset
         else:
-            Xy = np.dot(X.T, yw)
+            Xyw = np.dot(X.T, yw)
 
-    if Xy.ndim == 1:
-        Xy = Xy[:, np.newaxis]
+    if Xyw.ndim == 1:
+        Xyw = Xyw[:, np.newaxis]
     n_samples = X.shape[0]
-    alpha_max = np.max(np.sqrt(np.sum(Xy**2, axis=1))) / (l1_ratio * n_samples)
+    alpha_max = np.max(np.sqrt(np.sum(Xyw**2, axis=1))) / (l1_ratio * n_samples)
 
     if alpha_max <= np.finfo(float).resolution:
         return np.full(n_alphas, np.finfo(float).resolution)

From 2ba4c57acd36b7a6c92b7322ce0cb9fc18cd34ce Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Sat, 2 Jul 2022 09:51:25 -0400
Subject: [PATCH 06/33] Use Xy if it is provided.

---
 sklearn/linear_model/_coordinate_descent.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index 3655bd667c2d0..b1597bedce814 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -149,7 +149,7 @@ def _alpha_grid(
             "your estimator with the appropriate `alphas=` "
             "argument."
         )
-    if Xy is not None and ((sample_weight is None) or (fit_intercept is False)):
+    if Xy is not None:
         Xyw = Xy
     else:
         X, y, X_offset, _, _ = _preprocess_data(
@@ -162,7 +162,7 @@ def _alpha_grid(
             check_input=False,
         )
         if sample_weight is not None:
-            yw = y * sample_weight / sample_weight.mean()
+            yw = y * sample_weight
         else:
             yw = y
         if sparse.issparse(X):
@@ -172,7 +172,10 @@ def _alpha_grid(
 
     if Xyw.ndim == 1:
         Xyw = Xyw[:, np.newaxis]
-    n_samples = X.shape[0]
+    if sample_weight is not None:
+        n_samples = sample_weight.sum()
+    else:
+        n_samples = X.shape[0]
     alpha_max = np.max(np.sqrt(np.sum(Xyw**2, axis=1))) / (l1_ratio * n_samples)
 
     if alpha_max <= np.finfo(float).resolution:

From 5d1f5e7b09aa08cdf5b7d980ef114bcafb9f8c52 Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Sat, 2 Jul 2022 10:08:08 -0400
Subject: [PATCH 07/33] Update test, check alpha_max is not too large

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 807429985dcac..8fdc9dbc43c96 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -26,6 +26,7 @@
 from sklearn.utils._testing import assert_almost_equal
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import assert_array_equal
+from sklearn.utils._testing import assert_array_less
 from sklearn.utils._testing import ignore_warnings
 from sklearn.utils._testing import _convert_container
 
@@ -1651,9 +1652,14 @@ def test_enet_alpha_max_sample_weight(sparseX, fit_intercept):
     sample_weight = np.array([10, 1, 10, 1])
     if sparseX:
         X = sparse.csc_matrix(X)
+    # Test alpha_max makes coefs zero.
     reg = ElasticNetCV(n_alphas=1, cv=2, eps=1, fit_intercept=fit_intercept)
     reg.fit(X, y, sample_weight=sample_weight)
     assert_almost_equal(reg.coef_, 0)
+    # Test smaller alpha makes coefs nonzero.
+    reg = ElasticNetCV(n_alphas=2, cv=2, eps=0.99, fit_intercept=fit_intercept)
+    reg.fit(X, y, sample_weight=sample_weight)
+    assert_array_less(0, np.max(np.abs(reg.coef_)))
 
 
 @pytest.mark.parametrize("fit_intercept", [True, False])

From dce169c41c4613e50fbf61ac18883722edb47ffe Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Sat, 2 Jul 2022 12:43:15 -0400
Subject: [PATCH 08/33] Fix test that alpha_max is not too large.

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 8fdc9dbc43c96..a414a14c71c06 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1656,10 +1656,11 @@ def test_enet_alpha_max_sample_weight(sparseX, fit_intercept):
     reg = ElasticNetCV(n_alphas=1, cv=2, eps=1, fit_intercept=fit_intercept)
     reg.fit(X, y, sample_weight=sample_weight)
     assert_almost_equal(reg.coef_, 0)
+    alpha_max = reg.alpha_
     # Test smaller alpha makes coefs nonzero.
-    reg = ElasticNetCV(n_alphas=2, cv=2, eps=0.99, fit_intercept=fit_intercept)
+    reg = ElasticNet(alpha=0.99 * alpha_max, fit_intercept=fit_intercept)
     reg.fit(X, y, sample_weight=sample_weight)
-    assert_array_less(0, np.max(np.abs(reg.coef_)))
+    assert_array_less(1e-3, np.max(np.abs(reg.coef_)))
 
 
 @pytest.mark.parametrize("fit_intercept", [True, False])

From 380c21f6f342b69021b0f5269e919c2cb82980c9 Mon Sep 17 00:00:00 2001
From: "Mr. Snrub" <45150804+s-banach@users.noreply.github.com>
Date: Tue, 5 Jul 2022 22:39:17 -0400
Subject: [PATCH 09/33] Test alpha_max without sample_weight.

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index a414a14c71c06..c7c4a839b7170 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1645,11 +1645,11 @@ def test_enet_cv_grid_search(sample_weight):
 
 @pytest.mark.parametrize("sparseX", [False, True])
 @pytest.mark.parametrize("fit_intercept", [False, True])
-def test_enet_alpha_max_sample_weight(sparseX, fit_intercept):
-    X = np.array([[3, 1], [2, 5], [5, 3], [1, 4]])
+@pytest.mark.parametrize("sample_weight", [np.array([10, 1, 10, 1]), None])
+def test_enet_alpha_max_sample_weight(sparseX, fit_intercept, sample_weight):
+    X = np.array([[3.0, 1.0], [2.0, 5.0], [5.0, 3.0], [1.0, 4.0]])
     beta = np.array([1, 1])
     y = X @ beta
-    sample_weight = np.array([10, 1, 10, 1])
     if sparseX:
         X = sparse.csc_matrix(X)
     # Test alpha_max makes coefs zero.

From 40d8b304ffd615307638959e308a1e8ea53d3d89 Mon Sep 17 00:00:00 2001
From: snath-xoc <51656807+snath-xoc@users.noreply.github.com>
Date: Thu, 20 Jun 2024 22:10:54 +0100
Subject: [PATCH 10/33] Update _preprocess_data inputs in
 _coordinate_descent.py

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
---
 sklearn/linear_model/_coordinate_descent.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index b794065d04512..39143002b2638 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -149,7 +149,7 @@ def _alpha_grid(
         X, y, X_offset, _, _ = _preprocess_data(
             X,
             y,
-            fit_intercept,
+            fit_intercept=fit_intercept,
             copy=copy_X,
             sample_weight=sample_weight,
             check_input=False,

From 85062c0e37d52737f35dbbc090ec76b25cb22012 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Fri, 21 Jun 2024 00:34:19 +0100
Subject: [PATCH 11/33] added tests for repeated vs weighted on cyclic
 ElasticNetCV and modified alpha_grid_ to accommodate for MultitaskCV y shape

---
 sklearn/linear_model/_coordinate_descent.py   |  7 +++++-
 .../tests/test_coordinate_descent.py          | 22 +++++++++++++++++++
 2 files changed, 28 insertions(+), 1 deletion(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index 39143002b2638..fd44282f1aea3 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -155,7 +155,12 @@ def _alpha_grid(
             check_input=False,
         )
         if sample_weight is not None:
-            yw = y * sample_weight
+            if y.ndim>1:
+
+                yw = y * np.broadcast_to(sample_weight.reshape(-1,1),y.shape)
+            
+            else:
+                yw = y * sample_weight
         else:
             yw = y
         if sparse.issparse(X):
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index d294acf3141c5..1b41d02fb4e1a 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1305,6 +1305,28 @@ def test_enet_sample_weight_consistency(
     assert_allclose(reg1.coef_, reg2.coef_, rtol=1e-6)
 
 
+@pytest.mark.parametrize("fit_intercept", [True, False])
+@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
+def test_enet_cv_sample_weight(fit_intercept, sparse_container, global_random_seed):
+    rng = np.random.RandomState(global_random_seed)
+    rng = np.random.RandomState(0)
+
+    X, y = make_regression(
+            n_samples=10000, n_features=5, random_state=10
+        )
+
+    sample_weight = rng.randint(0, 5, size=X.shape[0])
+    X_resampled_by_weights = np.repeat(X, sample_weight, axis=0)
+    y_resampled_by_weights = np.repeat(y, sample_weight, axis=0)
+
+    est_weighted = ElasticNetCV(selection='cyclic').fit(X,y,sample_weight=sample_weight)
+    est_repeated = ElasticNetCV(selection='cyclic').fit(X_resampled_by_weights,y_resampled_by_weights)
+
+    assert_allclose(est_weighted.alphas_, est_repeated.alphas_)
+    assert_allclose(est_weighted.coef_, est_repeated.coef_)
+
+
+
 @pytest.mark.parametrize("fit_intercept", [True, False])
 @pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
 def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):

From 41fcb5f76bcbc3c3f095f65377ed5d6f23a4dbea Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Thu, 27 Jun 2024 19:38:32 +0200
Subject: [PATCH 12/33] added to changelog and changed seeding in tests

---
 doc/whats_new/v1.6.rst                             |  6 ++++++
 sklearn/cluster/_birch.py                          |  8 +++++---
 sklearn/discriminant_analysis.py                   |  8 +++++---
 sklearn/linear_model/_coordinate_descent.py        | 13 ++++---------
 sklearn/linear_model/_theil_sen.py                 |  8 +++++---
 .../linear_model/tests/test_coordinate_descent.py  | 14 +++++++-------
 sklearn/metrics/tests/test_common.py               |  1 -
 7 files changed, 32 insertions(+), 26 deletions(-)

diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
index e1e7a1f01f2f8..8d0fca344d193 100644
--- a/doc/whats_new/v1.6.rst
+++ b/doc/whats_new/v1.6.rst
@@ -99,6 +99,12 @@ Changelog
     :pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
     where 123455 is the *pull request* number, not the issue number.
 
+:mod:`sklearn.linear_model`
+..........................
+- |Fix| :func:`_coordinate_descent._alpha_grid` adapted to account for sample weights.
+  :pr:`23045`by :user:`John Hopfensperger <s-banach>`and :pr:`29308`by :user:`Shruti Nath <snath-xoc>`.
+
+
 :mod:`sklearn.base`
 ...................
 
diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
index 81c26d51bbb71..a69aa4711e221 100644
--- a/sklearn/cluster/_birch.py
+++ b/sklearn/cluster/_birch.py
@@ -531,9 +531,11 @@ def _fit(self, X, partial):
 
         if self.copy != "deprecated" and first_call:
             warnings.warn(
-                "`copy` was deprecated in 1.6 and will be removed in 1.8 since it "
-                "has no effect internally. Simply leave this parameter to its default "
-                "value to avoid this warning.",
+                (
+                    "`copy` was deprecated in 1.6 and will be removed in 1.8 since it"
+                    " has no effect internally. Simply leave this parameter to its"
+                    " default value to avoid this warning."
+                ),
                 FutureWarning,
             )
 
diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
index caad2de01b135..2b728ea1dd499 100644
--- a/sklearn/discriminant_analysis.py
+++ b/sklearn/discriminant_analysis.py
@@ -943,9 +943,11 @@ def fit(self, X, y):
             rank = np.sum(S2 > self.tol)
             if rank < n_features:
                 warnings.warn(
-                    f"The covariance matrix of class {ind} is not full rank. "
-                    "Increasing the value of parameter `reg_param` might help"
-                    " reducing the collinearity.",
+                    (
+                        f"The covariance matrix of class {ind} is not full rank. "
+                        "Increasing the value of parameter `reg_param` might help"
+                        " reducing the collinearity."
+                    ),
                     linalg.LinAlgWarning,
                 )
             if self.store_covariance or store_covariance:
diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index a896614c7efcb..ca05f0bb8337c 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -155,10 +155,9 @@ def _alpha_grid(
             check_input=False,
         )
         if sample_weight is not None:
-            if y.ndim>1:
+            if y.ndim > 1:
+                yw = y * np.broadcast_to(sample_weight.reshape(-1, 1), y.shape)
 
-                yw = y * np.broadcast_to(sample_weight.reshape(-1,1),y.shape)
-            
             else:
                 yw = y * sample_weight
         else:
@@ -176,8 +175,8 @@ def _alpha_grid(
         n_samples = X.shape[0]
     alpha_max = np.sqrt(np.sum(Xyw**2, axis=1)).max() / (n_samples * l1_ratio)
 
-    if alpha_max <= np.finfo(float).resolution:
-        return np.full(n_alphas, np.finfo(float).resolution)
+    if alpha_max <= np.finfo(np.float64).resolution:
+        return np.full(n_alphas, np.finfo(np.float64).resolution)
 
     return np.geomspace(alpha_max, alpha_max * eps, num=n_alphas)
 
@@ -980,7 +979,6 @@ def fit(self, X, y, sample_weight=None, check_input=True):
                 accept_sparse="csc",
                 order="F",
                 dtype=[np.float64, np.float32],
-                force_writeable=True,
                 accept_large_sparse=False,
                 copy=X_copied,
                 multi_output=True,
@@ -1609,7 +1607,6 @@ def fit(self, X, y, sample_weight=None, **params):
             check_X_params = dict(
                 accept_sparse="csc",
                 dtype=[np.float64, np.float32],
-                force_writeable=True,
                 copy=False,
                 accept_large_sparse=False,
             )
@@ -1635,7 +1632,6 @@ def fit(self, X, y, sample_weight=None, **params):
                 accept_sparse="csc",
                 dtype=[np.float64, np.float32],
                 order="F",
-                force_writeable=True,
                 copy=copy_X,
             )
             X, y = self._validate_data(
@@ -2513,7 +2509,6 @@ def fit(self, X, y):
         check_X_params = dict(
             dtype=[np.float64, np.float32],
             order="F",
-            force_writeable=True,
             copy=self.copy_X and self.fit_intercept,
         )
         check_y_params = dict(ensure_2d=False, order="F")
diff --git a/sklearn/linear_model/_theil_sen.py b/sklearn/linear_model/_theil_sen.py
index 61ae234d1d3fa..c85de38306ed8 100644
--- a/sklearn/linear_model/_theil_sen.py
+++ b/sklearn/linear_model/_theil_sen.py
@@ -416,9 +416,11 @@ def fit(self, X, y):
         """
         if self.copy_X != "deprecated":
             warnings.warn(
-                "`copy_X` was deprecated in 1.6 and will be removed in 1.8 since it "
-                "has no effect internally. Simply leave this parameter to its default "
-                "value to avoid this warning.",
+                (
+                    "`copy_X` was deprecated in 1.6 and will be removed in 1.8 since it"
+                    " has no effect internally. Simply leave this parameter to its"
+                    " default value to avoid this warning."
+                ),
                 FutureWarning,
             )
 
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 1b41d02fb4e1a..0cd4de25262b9 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1309,24 +1309,24 @@ def test_enet_sample_weight_consistency(
 @pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
 def test_enet_cv_sample_weight(fit_intercept, sparse_container, global_random_seed):
     rng = np.random.RandomState(global_random_seed)
-    rng = np.random.RandomState(0)
 
-    X, y = make_regression(
-            n_samples=10000, n_features=5, random_state=10
-        )
+    X, y = make_regression(n_samples=10000, n_features=5, random_state=rng)
 
     sample_weight = rng.randint(0, 5, size=X.shape[0])
     X_resampled_by_weights = np.repeat(X, sample_weight, axis=0)
     y_resampled_by_weights = np.repeat(y, sample_weight, axis=0)
 
-    est_weighted = ElasticNetCV(selection='cyclic').fit(X,y,sample_weight=sample_weight)
-    est_repeated = ElasticNetCV(selection='cyclic').fit(X_resampled_by_weights,y_resampled_by_weights)
+    est_weighted = ElasticNetCV(selection="cyclic", random_state=rng).fit(
+        X, y, sample_weight=sample_weight
+    )
+    est_repeated = ElasticNetCV(selection="cyclic",  random_state=rng).fit(
+        X_resampled_by_weights, y_resampled_by_weights
+    )
 
     assert_allclose(est_weighted.alphas_, est_repeated.alphas_)
     assert_allclose(est_weighted.coef_, est_repeated.coef_)
 
 
-
 @pytest.mark.parametrize("fit_intercept", [True, False])
 @pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
 def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):
diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
index c247e5f87f7d3..a85d9aebde23d 100644
--- a/sklearn/metrics/tests/test_common.py
+++ b/sklearn/metrics/tests/test_common.py
@@ -1950,7 +1950,6 @@ def check_array_api_regression_metric_multioutput(
 
 
 def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name):
-
     X_np = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=dtype_name)
     Y_np = np.array([[0.2, 0.3, 0.4], [0.5, 0.6, 0.7]], dtype=dtype_name)
 

From 335137d102c578fbc65e9a8f24a111588b374e35 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Thu, 27 Jun 2024 19:54:46 +0200
Subject: [PATCH 13/33] [all random seeds] test_enet_cv_sample_weight


From fec4f74034026c772da3715d0cb00039e4b7660b Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Fri, 28 Jun 2024 11:32:36 +0200
Subject: [PATCH 14/33] Revert unrelated changes

---
 sklearn/cluster/_birch.py            | 8 +++-----
 sklearn/discriminant_analysis.py     | 8 +++-----
 sklearn/linear_model/_theil_sen.py   | 8 +++-----
 sklearn/metrics/tests/test_common.py | 1 +
 4 files changed, 10 insertions(+), 15 deletions(-)

diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
index a69aa4711e221..81c26d51bbb71 100644
--- a/sklearn/cluster/_birch.py
+++ b/sklearn/cluster/_birch.py
@@ -531,11 +531,9 @@ def _fit(self, X, partial):
 
         if self.copy != "deprecated" and first_call:
             warnings.warn(
-                (
-                    "`copy` was deprecated in 1.6 and will be removed in 1.8 since it"
-                    " has no effect internally. Simply leave this parameter to its"
-                    " default value to avoid this warning."
-                ),
+                "`copy` was deprecated in 1.6 and will be removed in 1.8 since it "
+                "has no effect internally. Simply leave this parameter to its default "
+                "value to avoid this warning.",
                 FutureWarning,
             )
 
diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
index 2b728ea1dd499..caad2de01b135 100644
--- a/sklearn/discriminant_analysis.py
+++ b/sklearn/discriminant_analysis.py
@@ -943,11 +943,9 @@ def fit(self, X, y):
             rank = np.sum(S2 > self.tol)
             if rank < n_features:
                 warnings.warn(
-                    (
-                        f"The covariance matrix of class {ind} is not full rank. "
-                        "Increasing the value of parameter `reg_param` might help"
-                        " reducing the collinearity."
-                    ),
+                    f"The covariance matrix of class {ind} is not full rank. "
+                    "Increasing the value of parameter `reg_param` might help"
+                    " reducing the collinearity.",
                     linalg.LinAlgWarning,
                 )
             if self.store_covariance or store_covariance:
diff --git a/sklearn/linear_model/_theil_sen.py b/sklearn/linear_model/_theil_sen.py
index c85de38306ed8..61ae234d1d3fa 100644
--- a/sklearn/linear_model/_theil_sen.py
+++ b/sklearn/linear_model/_theil_sen.py
@@ -416,11 +416,9 @@ def fit(self, X, y):
         """
         if self.copy_X != "deprecated":
             warnings.warn(
-                (
-                    "`copy_X` was deprecated in 1.6 and will be removed in 1.8 since it"
-                    " has no effect internally. Simply leave this parameter to its"
-                    " default value to avoid this warning."
-                ),
+                "`copy_X` was deprecated in 1.6 and will be removed in 1.8 since it "
+                "has no effect internally. Simply leave this parameter to its default "
+                "value to avoid this warning.",
                 FutureWarning,
             )
 
diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
index a85d9aebde23d..c247e5f87f7d3 100644
--- a/sklearn/metrics/tests/test_common.py
+++ b/sklearn/metrics/tests/test_common.py
@@ -1950,6 +1950,7 @@ def check_array_api_regression_metric_multioutput(
 
 
 def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name):
+
     X_np = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=dtype_name)
     Y_np = np.array([[0.2, 0.3, 0.4], [0.5, 0.6, 0.7]], dtype=dtype_name)
 

From c41a8eeabdb42ffca30f36236d6d197362d340a7 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Fri, 28 Jun 2024 18:10:54 +0200
Subject: [PATCH 15/33] merged test into test_enet_cv_sample_weight_correctness

---
 .../tests/test_coordinate_descent.py          | 51 ++++---------------
 1 file changed, 11 insertions(+), 40 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 0cd4de25262b9..b60efd91e72e9 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1305,28 +1305,6 @@ def test_enet_sample_weight_consistency(
     assert_allclose(reg1.coef_, reg2.coef_, rtol=1e-6)
 
 
-@pytest.mark.parametrize("fit_intercept", [True, False])
-@pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
-def test_enet_cv_sample_weight(fit_intercept, sparse_container, global_random_seed):
-    rng = np.random.RandomState(global_random_seed)
-
-    X, y = make_regression(n_samples=10000, n_features=5, random_state=rng)
-
-    sample_weight = rng.randint(0, 5, size=X.shape[0])
-    X_resampled_by_weights = np.repeat(X, sample_weight, axis=0)
-    y_resampled_by_weights = np.repeat(y, sample_weight, axis=0)
-
-    est_weighted = ElasticNetCV(selection="cyclic", random_state=rng).fit(
-        X, y, sample_weight=sample_weight
-    )
-    est_repeated = ElasticNetCV(selection="cyclic",  random_state=rng).fit(
-        X_resampled_by_weights, y_resampled_by_weights
-    )
-
-    assert_allclose(est_weighted.alphas_, est_repeated.alphas_)
-    assert_allclose(est_weighted.coef_, est_repeated.coef_)
-
-
 @pytest.mark.parametrize("fit_intercept", [True, False])
 @pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
 def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):
@@ -1342,39 +1320,32 @@ def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):
         X = sparse_container(X)
     params = dict(tol=1e-6)
 
-    # Set alphas, otherwise the two cv models might use different ones.
-    if fit_intercept:
-        alphas = np.linspace(0.001, 0.01, num=91)
-    else:
-        alphas = np.linspace(0.01, 0.1, num=91)
-
-    # We weight the first fold 2 times more.
-    sw[:n_samples] = 2
+    # We weight the first fold n times more.
+    sw[:n_samples] = rng.randint(0, 5, size=sw[:n_samples].shape[0])
     groups_sw = np.r_[
         np.full(n_samples, 0), np.full(n_samples, 1), np.full(n_samples, 2)
     ]
     splits_sw = list(LeaveOneGroupOut().split(X, groups=groups_sw))
-    reg_sw = ElasticNetCV(
-        alphas=alphas, cv=splits_sw, fit_intercept=fit_intercept, **params
-    )
+    reg_sw = ElasticNetCV(cv=splits_sw, fit_intercept=fit_intercept, **params)
     reg_sw.fit(X, y, sample_weight=sw)
 
     # We repeat the first fold 2 times and provide splits ourselves
     if sparse_container is not None:
         X = X.toarray()
-    X = np.r_[X[:n_samples], X]
+    X_rep = np.repeat(X[:n_samples], sw, axis=0)
+    ##Need to know number of repitions made in total
+    n_reps = X_rep.shape[0] - X.shape[0]
+    X = X_rep
     if sparse_container is not None:
         X = sparse_container(X)
-    y = np.r_[y[:n_samples], y]
-    groups = np.r_[
-        np.full(2 * n_samples, 0), np.full(n_samples, 1), np.full(n_samples, 2)
-    ]
+    y = np.repeat(y[:n_samples], sw, axis=0)
+    groups = np.r_[np.full(n_reps, 0), np.full(n_samples, 1), np.full(n_samples, 2)]
     splits = list(LeaveOneGroupOut().split(X, groups=groups))
-    reg = ElasticNetCV(alphas=alphas, cv=splits, fit_intercept=fit_intercept, **params)
+    reg = ElasticNetCV(cv=splits, fit_intercept=fit_intercept, **params)
     reg.fit(X, y)
 
     # ensure that we chose meaningful alphas, i.e. not boundaries
-    assert alphas[0] < reg.alpha_ < alphas[-1]
+    assert_allclose(reg_sw.alphas_, reg.alphas_)
     assert reg_sw.alpha_ == reg.alpha_
     assert_allclose(reg_sw.coef_, reg.coef_)
     assert reg_sw.intercept_ == pytest.approx(reg.intercept_)

From ac9f0908a79de774e75be4219c53c12819e6ef2a Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Sat, 29 Jun 2024 17:22:24 +0200
Subject: [PATCH 16/33] changed sample weight to be explicitly set as integers
 in
 sklearn/linear_model/tests/test_coordinate_descent/test_enet_cv_sample_weight_correctness

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index b60efd91e72e9..4e5a7c634c87a 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1332,14 +1332,16 @@ def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):
     # We repeat the first fold 2 times and provide splits ourselves
     if sparse_container is not None:
         X = X.toarray()
-    X_rep = np.repeat(X[:n_samples], sw, axis=0)
+    X_rep = np.repeat(X, sw.astype(int), axis=0)
     ##Need to know number of repitions made in total
     n_reps = X_rep.shape[0] - X.shape[0]
     X = X_rep
     if sparse_container is not None:
         X = sparse_container(X)
-    y = np.repeat(y[:n_samples], sw, axis=0)
-    groups = np.r_[np.full(n_reps, 0), np.full(n_samples, 1), np.full(n_samples, 2)]
+    y = np.repeat(y, sw.astype(int), axis=0)
+    groups = np.r_[
+        np.full(n_reps + n_samples, 0), np.full(n_samples, 1), np.full(n_samples, 2)
+    ]
     splits = list(LeaveOneGroupOut().split(X, groups=groups))
     reg = ElasticNetCV(cv=splits, fit_intercept=fit_intercept, **params)
     reg.fit(X, y)

From 2a42de2c768f35e68901e2f9af96e503b3834c88 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@Shrutis-Laptop.local>
Date: Fri, 5 Jul 2024 12:03:04 +0200
Subject: [PATCH 17/33] add sample weights into default score calculation under
 _log_reg_scoring_path

---
 sklearn/linear_model/_logistic.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index 23daa86f2146d..39938114009fb 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -741,9 +741,11 @@ def _log_reg_scoring_path(
     y_train = y[train]
     y_test = y[test]
 
+    sw_train, sw_test = None, None
     if sample_weight is not None:
         sample_weight = _check_sample_weight(sample_weight, X)
-        sample_weight = sample_weight[train]
+        sw_train = sample_weight[train]
+        sw_test = sample_weight[test]
 
     coefs, Cs, n_iter = _logistic_regression_path(
         X_train,
@@ -764,7 +766,7 @@ def _log_reg_scoring_path(
         random_state=random_state,
         check_input=False,
         max_squared_sum=max_squared_sum,
-        sample_weight=sample_weight,
+        sample_weight=sw_train,
     )
 
     log_reg = LogisticRegression(solver=solver, multi_class=multi_class)
@@ -798,12 +800,11 @@ def _log_reg_scoring_path(
             log_reg.intercept_ = 0.0
 
         if scoring is None:
-            scores.append(log_reg.score(X_test, y_test))
+            scores.append(log_reg.score(X_test, y_test, sample_weight=sw_test))
         else:
             score_params = score_params or {}
             score_params = _check_method_params(X=X, params=score_params, indices=test)
             scores.append(scoring(log_reg, X_test, y_test, **score_params))
-
     return coefs, Cs, np.array(scores), n_iter
 
 

From ff02ffadb2bd43afb2b371b98aaca4a3784f6a11 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Fri, 5 Jul 2024 16:39:04 +0200
Subject: [PATCH 18/33] [all random seeds] test_enet_cv_sample_weight


From 63ad46b3068fe8f8a65d54616bb3f9ababb1eae2 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Fri, 5 Jul 2024 16:43:49 +0200
Subject: [PATCH 19/33] [all random seeds] test_enet_cv_sample_weight


From 2dc37c9f6091abd92cb3320ec56a2360050d5965 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Tue, 9 Jul 2024 20:46:54 +0100
Subject: [PATCH 20/33] modified test_coordinate_descent for X, y, group names

---
 sklearn/linear_model/_coordinate_descent.py   |  4 ++
 .../tests/test_coordinate_descent.py          | 60 +++++++++++--------
 2 files changed, 38 insertions(+), 26 deletions(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index ca05f0bb8337c..0f19636dcdb06 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -979,6 +979,7 @@ def fit(self, X, y, sample_weight=None, check_input=True):
                 accept_sparse="csc",
                 order="F",
                 dtype=[np.float64, np.float32],
+                force_writeable=True,
                 accept_large_sparse=False,
                 copy=X_copied,
                 multi_output=True,
@@ -1607,6 +1608,7 @@ def fit(self, X, y, sample_weight=None, **params):
             check_X_params = dict(
                 accept_sparse="csc",
                 dtype=[np.float64, np.float32],
+                force_writeable=True,
                 copy=False,
                 accept_large_sparse=False,
             )
@@ -1632,6 +1634,7 @@ def fit(self, X, y, sample_weight=None, **params):
                 accept_sparse="csc",
                 dtype=[np.float64, np.float32],
                 order="F",
+                force_writeable=True,
                 copy=copy_X,
             )
             X, y = self._validate_data(
@@ -2509,6 +2512,7 @@ def fit(self, X, y):
         check_X_params = dict(
             dtype=[np.float64, np.float32],
             order="F",
+            force_writeable=True,
             copy=self.copy_X and self.fit_intercept,
         )
         check_y_params = dict(ensure_2d=False, order="F")
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 4e5a7c634c87a..4b6ccb2d730dd 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1311,46 +1311,54 @@ def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):
     """Test that ElasticNetCV with sample weights gives correct results."""
     rng = np.random.RandomState(42)
     n_splits, n_samples, n_features = 3, 10, 5
-    X = rng.rand(n_splits * n_samples, n_features)
+    X_with_weights = rng.rand(n_splits * n_samples, n_features)
     beta = rng.rand(n_features)
     beta[0:2] = 0
-    y = X @ beta + rng.rand(n_splits * n_samples)
-    sw = np.ones_like(y)
+    y_with_weights = X_with_weights @ beta + rng.rand(n_splits * n_samples)
+    sw = np.ones_like(y_with_weights)
     if sparse_container is not None:
-        X = sparse_container(X)
+        X_with_weights = sparse_container(X_with_weights)
     params = dict(tol=1e-6)
 
-    # We weight the first fold n times more.
+    # Inspect the total number of random repetitions so as to adjust the size of
+    # the first cross-validation group accordingly.
     sw[:n_samples] = rng.randint(0, 5, size=sw[:n_samples].shape[0])
-    groups_sw = np.r_[
+    groups_with_weights = np.r_[
         np.full(n_samples, 0), np.full(n_samples, 1), np.full(n_samples, 2)
     ]
-    splits_sw = list(LeaveOneGroupOut().split(X, groups=groups_sw))
-    reg_sw = ElasticNetCV(cv=splits_sw, fit_intercept=fit_intercept, **params)
-    reg_sw.fit(X, y, sample_weight=sw)
+    splits_with_weights = list(
+        LeaveOneGroupOut().split(X_with_weights, groups=groups_with_weights)
+    )
+    reg_with_weights = ElasticNetCV(
+        cv=splits_with_weights, fit_intercept=fit_intercept, **params
+    )
+    reg_with_weights.fit(X_with_weights, y_with_weights, sample_weight=sw)
 
     # We repeat the first fold 2 times and provide splits ourselves
     if sparse_container is not None:
-        X = X.toarray()
-    X_rep = np.repeat(X, sw.astype(int), axis=0)
-    ##Need to know number of repitions made in total
-    n_reps = X_rep.shape[0] - X.shape[0]
-    X = X_rep
+        X_with_weights = X_with_weights.toarray()
+    X_with_repetitions = np.repeat(X_with_weights, sw.astype(int), axis=0)
+
     if sparse_container is not None:
-        X = sparse_container(X)
-    y = np.repeat(y, sw.astype(int), axis=0)
-    groups = np.r_[
-        np.full(n_reps + n_samples, 0), np.full(n_samples, 1), np.full(n_samples, 2)
-    ]
-    splits = list(LeaveOneGroupOut().split(X, groups=groups))
-    reg = ElasticNetCV(cv=splits, fit_intercept=fit_intercept, **params)
-    reg.fit(X, y)
+        X_with_repetitions = sparse_container(X_with_repetitions)
+
+    y_with_repetitions = np.repeat(y_with_weights, sw.astype(int), axis=0)
+    groups_with_repetitions = np.repeat(groups_with_weights, sw.astype(int), axis=0)
+
+    splits_with_repetitions = list(
+        LeaveOneGroupOut().split(X_with_repetitions, groups=groups_with_repetitions)
+    )
+    reg_with_repetitions = ElasticNetCV(
+        cv=splits_with_repetitions, fit_intercept=fit_intercept, **params
+    )
+    reg_with_repetitions.fit(X_with_repetitions, y_with_repetitions)
 
     # ensure that we chose meaningful alphas, i.e. not boundaries
-    assert_allclose(reg_sw.alphas_, reg.alphas_)
-    assert reg_sw.alpha_ == reg.alpha_
-    assert_allclose(reg_sw.coef_, reg.coef_)
-    assert reg_sw.intercept_ == pytest.approx(reg.intercept_)
+    assert_allclose(reg_with_weights.mse_path_, reg_with_repetitions.mse_path_)
+    assert_allclose(reg_with_weights.alphas_, reg_with_repetitions.alphas_)
+    assert reg_with_weights.alpha_ == reg_with_repetitions.alpha_
+    assert_allclose(reg_with_weights.coef_, reg_with_repetitions.coef_)
+    assert reg_with_weights.intercept_ == pytest.approx(reg_with_repetitions.intercept_)
 
 
 @pytest.mark.parametrize("sample_weight", [False, True])

From 7cdbf52f2a74fb883d69dbc3ff151e9db5db8a00 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Tue, 9 Jul 2024 21:11:47 +0100
Subject: [PATCH 21/33] restoring some files

---
 sklearn/linear_model/_logistic.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index 39938114009fb..23daa86f2146d 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -741,11 +741,9 @@ def _log_reg_scoring_path(
     y_train = y[train]
     y_test = y[test]
 
-    sw_train, sw_test = None, None
     if sample_weight is not None:
         sample_weight = _check_sample_weight(sample_weight, X)
-        sw_train = sample_weight[train]
-        sw_test = sample_weight[test]
+        sample_weight = sample_weight[train]
 
     coefs, Cs, n_iter = _logistic_regression_path(
         X_train,
@@ -766,7 +764,7 @@ def _log_reg_scoring_path(
         random_state=random_state,
         check_input=False,
         max_squared_sum=max_squared_sum,
-        sample_weight=sw_train,
+        sample_weight=sample_weight,
     )
 
     log_reg = LogisticRegression(solver=solver, multi_class=multi_class)
@@ -800,11 +798,12 @@ def _log_reg_scoring_path(
             log_reg.intercept_ = 0.0
 
         if scoring is None:
-            scores.append(log_reg.score(X_test, y_test, sample_weight=sw_test))
+            scores.append(log_reg.score(X_test, y_test))
         else:
             score_params = score_params or {}
             score_params = _check_method_params(X=X, params=score_params, indices=test)
             scores.append(scoring(log_reg, X_test, y_test, **score_params))
+
     return coefs, Cs, np.array(scores), n_iter
 
 

From 3a1957589f7dd56713ea9040b70228124dbd5fc7 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Tue, 9 Jul 2024 22:36:56 +0100
Subject: [PATCH 22/33] add global_random_seed

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 4b6ccb2d730dd..fffe9759fde0a 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1307,9 +1307,11 @@ def test_enet_sample_weight_consistency(
 
 @pytest.mark.parametrize("fit_intercept", [True, False])
 @pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS)
-def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):
+def test_enet_cv_sample_weight_correctness(
+    fit_intercept, sparse_container, global_random_seed
+):
     """Test that ElasticNetCV with sample weights gives correct results."""
-    rng = np.random.RandomState(42)
+    rng = np.random.RandomState(global_random_seed)
     n_splits, n_samples, n_features = 3, 10, 5
     X_with_weights = rng.rand(n_splits * n_samples, n_features)
     beta = rng.rand(n_features)
@@ -1322,7 +1324,7 @@ def test_enet_cv_sample_weight_correctness(fit_intercept, sparse_container):
 
     # Inspect the total number of random repetitions so as to adjust the size of
     # the first cross-validation group accordingly.
-    sw[:n_samples] = rng.randint(0, 5, size=sw[:n_samples].shape[0])
+    sw[:n_samples] = rng.randint(0, 5, size=n_samples)
     groups_with_weights = np.r_[
         np.full(n_samples, 0), np.full(n_samples, 1), np.full(n_samples, 2)
     ]

From f774c6acce08c80bde962cbf5b01117b22c84025 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Thu, 8 Aug 2024 20:13:27 -0400
Subject: [PATCH 23/33] updated comments and changelog

---
 doc/whats_new/v1.6.rst                        |  6 +-
 .../tests/test_coordinate_descent.py          | 83 +++++++++++--------
 2 files changed, 53 insertions(+), 36 deletions(-)

diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
index a145e614606d3..1a7164f955a4e 100644
--- a/doc/whats_new/v1.6.rst
+++ b/doc/whats_new/v1.6.rst
@@ -137,8 +137,10 @@ Changelog
 
 :mod:`sklearn.linear_model`
 ..........................
-- |Fix| :func:`_coordinate_descent._alpha_grid` adapted to account for sample weights.
-  :pr:`23045`by :user:`John Hopfensperger <s-banach>`and :pr:`29308`by :user:`Shruti Nath <snath-xoc>`.
+- |Fix| :class:`linear_model.LassoCV` and :class:`linear_model.ElasticNetCV` now
+take sample weights into accounts to define the search grid for the internally tuned
+`alpha` hyper-parameter. :pr:`29442` by :user:`John Hopfensperger <s-banach> and
+:user:`Shruti Nath <snath-xoc>`.
 
 
 :mod:`sklearn.base`
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index a7ba3c42e085c..0a0185a1e1878 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -265,7 +265,9 @@ def test_lasso_cv():
     )
     # check that they also give a similar MSE
     mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
-    assert_allclose(mse_lars(clf.alphas_[5]).mean(), clf.mse_path_[5].mean(), rtol=1e-2)
+    np.testing.assert_approx_equal(
+        mse_lars(clf.alphas_[5]).mean(), clf.mse_path_[5].mean(), significant=2
+    )
 
     # test set
     assert clf.score(X_test, y_test) > 0.99
@@ -1308,23 +1310,37 @@ def test_enet_sample_weight_consistency(
 def test_enet_cv_sample_weight_correctness(
     fit_intercept, sparse_container, global_random_seed
 ):
-    """Test that ElasticNetCV with sample weights gives correct results."""
+    """Test that ElasticNetCV with sample weights gives correct results.
+
+    We fit the same model twice, once with weighted training data, once with repeated
+    data points in the training data and check that both models converge to the
+    same solution.
+    Since this model uses an internal cross-validation scheme to tune the alpha
+    regularization parameter, we make sure that the repetitions only occur within
+    a specific CV group. Data points belonging to other CV groups stay
+    unit-weighted / "unrepeated".
+    """
     rng = np.random.RandomState(global_random_seed)
-    n_splits, n_samples, n_features = 3, 10, 5
-    X_with_weights = rng.rand(n_splits * n_samples, n_features)
+    n_splits, n_samples_per_cv, n_features = 3, 10, 5
+    X_with_weights = rng.rand(n_splits * n_samples_per_cv, n_features)
     beta = rng.rand(n_features)
     beta[0:2] = 0
-    y_with_weights = X_with_weights @ beta + rng.rand(n_splits * n_samples)
-    sw = np.ones_like(y_with_weights)
+    y_with_weights = X_with_weights @ beta + rng.rand(n_splits * n_samples_per_cv)
+
     if sparse_container is not None:
         X_with_weights = sparse_container(X_with_weights)
     params = dict(tol=1e-6)
 
-    # Inspect the total number of random repetitions so as to adjust the size of
-    # the first cross-validation group accordingly.
-    sw[:n_samples] = rng.randint(0, 5, size=n_samples)
+    # Assign random integer weights only to the first cross-validation group.
+    # The samples in the other cross-validation groups are left with unit
+    # weights.
+
+    sw = np.ones_like(y_with_weights)
+    sw[:n_samples_per_cv] = rng.randint(0, 5, size=n_samples_per_cv)
     groups_with_weights = np.r_[
-        np.full(n_samples, 0), np.full(n_samples, 1), np.full(n_samples, 2)
+        np.full(n_samples_per_cv, 0),
+        np.full(n_samples_per_cv, 1),
+        np.full(n_samples_per_cv, 2),
     ]
     splits_with_weights = list(
         LeaveOneGroupOut().split(X_with_weights, groups=groups_with_weights)
@@ -1332,13 +1348,12 @@ def test_enet_cv_sample_weight_correctness(
     reg_with_weights = ElasticNetCV(
         cv=splits_with_weights, fit_intercept=fit_intercept, **params
     )
+
     reg_with_weights.fit(X_with_weights, y_with_weights, sample_weight=sw)
 
-    # We repeat the first fold 2 times and provide splits ourselves
     if sparse_container is not None:
         X_with_weights = X_with_weights.toarray()
     X_with_repetitions = np.repeat(X_with_weights, sw.astype(int), axis=0)
-
     if sparse_container is not None:
         X_with_repetitions = sparse_container(X_with_repetitions)
 
@@ -1356,7 +1371,7 @@ def test_enet_cv_sample_weight_correctness(
     # ensure that we chose meaningful alphas, i.e. not boundaries
     assert_allclose(reg_with_weights.mse_path_, reg_with_repetitions.mse_path_)
     assert_allclose(reg_with_weights.alphas_, reg_with_repetitions.alphas_)
-    assert reg_with_weights.alpha_ == reg_with_repetitions.alpha_
+    assert reg_with_weights.alpha_ == pytest.approx(reg_with_repetitions.alpha_)
     assert_allclose(reg_with_weights.coef_, reg_with_repetitions.coef_)
     assert reg_with_weights.intercept_ == pytest.approx(reg_with_repetitions.intercept_)
 
@@ -1396,26 +1411,6 @@ def test_enet_cv_grid_search(sample_weight):
     assert reg.alpha_ == pytest.approx(gs.best_params_["alpha"])
 
 
-@pytest.mark.parametrize("sparseX", [False, True])
-@pytest.mark.parametrize("fit_intercept", [False, True])
-@pytest.mark.parametrize("sample_weight", [np.array([10, 1, 10, 1]), None])
-def test_enet_alpha_max_sample_weight(sparseX, fit_intercept, sample_weight):
-    X = np.array([[3.0, 1.0], [2.0, 5.0], [5.0, 3.0], [1.0, 4.0]])
-    beta = np.array([1, 1])
-    y = X @ beta
-    if sparseX:
-        X = sparse.csc_matrix(X)
-    # Test alpha_max makes coefs zero.
-    reg = ElasticNetCV(n_alphas=1, cv=2, eps=1, fit_intercept=fit_intercept)
-    reg.fit(X, y, sample_weight=sample_weight)
-    assert_almost_equal(reg.coef_, 0)
-    alpha_max = reg.alpha_
-    # Test smaller alpha makes coefs nonzero.
-    reg = ElasticNet(alpha=0.99 * alpha_max, fit_intercept=fit_intercept)
-    reg.fit(X, y, sample_weight=sample_weight)
-    assert_array_less(1e-3, np.max(np.abs(reg.coef_)))
-
-
 @pytest.mark.parametrize("fit_intercept", [True, False])
 @pytest.mark.parametrize("l1_ratio", [0, 0.5, 1])
 @pytest.mark.parametrize("precompute", [False, True])
@@ -1470,9 +1465,29 @@ def test_enet_cv_sample_weight_consistency(
         assert_allclose(reg.intercept_, intercept)
 
 
+@pytest.mark.parametrize("X_is_sparse", [False, True])
+@pytest.mark.parametrize("fit_intercept", [False, True])
+@pytest.mark.parametrize("sample_weight", [np.array([10, 1, 10, 1]), None])
+def test_enet_alpha_max_sample_weight(X_is_sparse, fit_intercept, sample_weight):
+    X = np.array([[3.0, 1.0], [2.0, 5.0], [5.0, 3.0], [1.0, 4.0]])
+    beta = np.array([1, 1])
+    y = X @ beta
+    if X_is_sparse:
+        X = sparse.csc_matrix(X)
+    # Test alpha_max makes coefs zero.
+    reg = ElasticNetCV(n_alphas=1, cv=2, eps=1, fit_intercept=fit_intercept)
+    reg.fit(X, y, sample_weight=sample_weight)
+    assert_almost_equal(reg.coef_, 0)
+    alpha_max = reg.alpha_
+    # Test smaller alpha makes coefs nonzero.
+    reg = ElasticNet(alpha=0.99 * alpha_max, fit_intercept=fit_intercept)
+    reg.fit(X, y, sample_weight=sample_weight)
+    assert_array_less(1e-3, np.max(np.abs(reg.coef_)))
+
+
 @pytest.mark.parametrize("estimator", [ElasticNetCV, LassoCV])
 def test_linear_models_cv_fit_with_loky(estimator):
-    # LinearModelsCV.fit performs inplace operations on fancy-indexed memmapped
+    # LinearModelsCV.fit performs operations on fancy-indexed memmapped
     # data when using the loky backend, causing an error due to unexpected
     # behavior of fancy indexing of read-only memmaps (cf. numpy#14132).
 

From 69646d9009d82e13f6ffec09b7a7da97adfa08e4 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Thu, 8 Aug 2024 20:17:13 -0400
Subject: [PATCH 24/33] [all random seeds]

---
 sklearn/linear_model/_coordinate_descent.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index 27e430c698530..f94f9495d5a76 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -134,7 +134,7 @@ def _alpha_grid(
     copy_X : bool, default=True
         If ``True``, X will be copied; else, it may be overwritten.
 
-    sample_weight : ndarray of shape (n_samples,)
+    sample_weight : ndarray of shape (n_samples,), default=None
     """
     if l1_ratio == 0:
         raise ValueError(

From d71315cfa4b3effd1c4a06ccdc2446b3fd39ae7e Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Wed, 21 Aug 2024 15:47:48 +0300
Subject: [PATCH 25/33] [all random seeds]
 test_enet_cv_sample_weight_correctness

---
 doc/whats_new/v1.6.rst                                | 9 ++++-----
 sklearn/linear_model/tests/test_coordinate_descent.py | 2 +-
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
index 1a7164f955a4e..b85efb2ec48eb 100644
--- a/doc/whats_new/v1.6.rst
+++ b/doc/whats_new/v1.6.rst
@@ -136,12 +136,11 @@ Changelog
     where 123455 is the *pull request* number, not the issue number.
 
 :mod:`sklearn.linear_model`
-..........................
+...........................
 - |Fix| :class:`linear_model.LassoCV` and :class:`linear_model.ElasticNetCV` now
-take sample weights into accounts to define the search grid for the internally tuned
-`alpha` hyper-parameter. :pr:`29442` by :user:`John Hopfensperger <s-banach> and
-:user:`Shruti Nath <snath-xoc>`.
-
+  take sample weights into accounts to define the search grid for the internally tuned
+  `alpha` hyper-parameter. :pr:`29442` by :user:`John Hopfensperger <s-banach> and
+  :user:`Shruti Nath <snath-xoc>`.
 
 :mod:`sklearn.base`
 ...................
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 0a0185a1e1878..5ba99fa44206b 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1372,7 +1372,7 @@ def test_enet_cv_sample_weight_correctness(
     assert_allclose(reg_with_weights.mse_path_, reg_with_repetitions.mse_path_)
     assert_allclose(reg_with_weights.alphas_, reg_with_repetitions.alphas_)
     assert reg_with_weights.alpha_ == pytest.approx(reg_with_repetitions.alpha_)
-    assert_allclose(reg_with_weights.coef_, reg_with_repetitions.coef_)
+    assert_allclose(reg_with_weights.coef_, reg_with_repetitions.coef_, atol=1e-10)
     assert reg_with_weights.intercept_ == pytest.approx(reg_with_repetitions.intercept_)
 
 

From 9d704956bc252b916e108541df6909e11ac0b5f0 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Wed, 21 Aug 2024 17:12:09 +0300
Subject: [PATCH 26/33] trigger regular CI


From d2c6c4d6b422ddee8b769ff40aafa274c8d1a126 Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Mon, 26 Aug 2024 11:03:40 +0300
Subject: [PATCH 27/33] [all random seeds] test_lasso_cv
 test_enet_alpha_max_sample_weight test_enet_cv_sample_weight_correctness

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 5ba99fa44206b..cd41341962fd8 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -265,9 +265,7 @@ def test_lasso_cv():
     )
     # check that they also give a similar MSE
     mse_lars = interpolate.interp1d(lars.cv_alphas_, lars.mse_path_.T)
-    np.testing.assert_approx_equal(
-        mse_lars(clf.alphas_[5]).mean(), clf.mse_path_[5].mean(), significant=2
-    )
+    assert_allclose(mse_lars(clf.alphas_[5]).mean(), clf.mse_path_[5].mean(), rtol=1e-2)
 
     # test set
     assert clf.score(X_test, y_test) > 0.99
@@ -1477,7 +1475,7 @@ def test_enet_alpha_max_sample_weight(X_is_sparse, fit_intercept, sample_weight)
     # Test alpha_max makes coefs zero.
     reg = ElasticNetCV(n_alphas=1, cv=2, eps=1, fit_intercept=fit_intercept)
     reg.fit(X, y, sample_weight=sample_weight)
-    assert_almost_equal(reg.coef_, 0)
+    assert_allclose(reg.coef_, 0, atol=1e-5)
     alpha_max = reg.alpha_
     # Test smaller alpha makes coefs nonzero.
     reg = ElasticNet(alpha=0.99 * alpha_max, fit_intercept=fit_intercept)

From 1f7a407e911b83a309eec45fc0ee4c547dfd1027 Mon Sep 17 00:00:00 2001
From: Shruti Nath <51656807+snath-xoc@users.noreply.github.com>
Date: Fri, 6 Sep 2024 10:52:07 +0200
Subject: [PATCH 28/33] Apply suggestions from code review

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
---
 sklearn/linear_model/tests/test_coordinate_descent.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index cd41341962fd8..5ad5b863c6ab9 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1313,6 +1313,7 @@ def test_enet_cv_sample_weight_correctness(
     We fit the same model twice, once with weighted training data, once with repeated
     data points in the training data and check that both models converge to the
     same solution.
+
     Since this model uses an internal cross-validation scheme to tune the alpha
     regularization parameter, we make sure that the repetitions only occur within
     a specific CV group. Data points belonging to other CV groups stay
@@ -1335,11 +1336,11 @@ def test_enet_cv_sample_weight_correctness(
 
     sw = np.ones_like(y_with_weights)
     sw[:n_samples_per_cv] = rng.randint(0, 5, size=n_samples_per_cv)
-    groups_with_weights = np.r_[
+    groups_with_weights = np.concatenate([
         np.full(n_samples_per_cv, 0),
         np.full(n_samples_per_cv, 1),
         np.full(n_samples_per_cv, 2),
-    ]
+    ])
     splits_with_weights = list(
         LeaveOneGroupOut().split(X_with_weights, groups=groups_with_weights)
     )

From 9aa6ff0f174222b3783671d635a5e47ae4f3ba5c Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Fri, 6 Sep 2024 12:27:19 +0200
Subject: [PATCH 29/33] Fix linting problem

---
 .../linear_model/tests/test_coordinate_descent.py    | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 5ad5b863c6ab9..501eacc2d24d1 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1336,11 +1336,13 @@ def test_enet_cv_sample_weight_correctness(
 
     sw = np.ones_like(y_with_weights)
     sw[:n_samples_per_cv] = rng.randint(0, 5, size=n_samples_per_cv)
-    groups_with_weights = np.concatenate([
-        np.full(n_samples_per_cv, 0),
-        np.full(n_samples_per_cv, 1),
-        np.full(n_samples_per_cv, 2),
-    ])
+    groups_with_weights = np.concatenate(
+        [
+            np.full(n_samples_per_cv, 0),
+            np.full(n_samples_per_cv, 1),
+            np.full(n_samples_per_cv, 2),
+        ]
+    )
     splits_with_weights = list(
         LeaveOneGroupOut().split(X_with_weights, groups=groups_with_weights)
     )

From cf31e146784912afddb0cd075b0622fd00eaa8fd Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Fri, 6 Sep 2024 17:05:14 +0200
Subject: [PATCH 30/33] updated changelog

---
 doc/whats_new/v1.6.rst                               | 12 +++++-------
 sklearn/linear_model/_coordinate_descent.py          |  3 +--
 .../linear_model/tests/test_coordinate_descent.py    |  1 -
 3 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
index 69910f0172509..790807f4848c7 100644
--- a/doc/whats_new/v1.6.rst
+++ b/doc/whats_new/v1.6.rst
@@ -146,13 +146,6 @@ Changelog
     :pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
     where 123455 is the *pull request* number, not the issue number.
 
-:mod:`sklearn.linear_model`
-...........................
-- |Fix| :class:`linear_model.LassoCV` and :class:`linear_model.ElasticNetCV` now
-  take sample weights into accounts to define the search grid for the internally tuned
-  `alpha` hyper-parameter. :pr:`29442` by :user:`John Hopfensperger <s-banach> and
-  :user:`Shruti Nath <snath-xoc>`.
-
 :mod:`sklearn.base`
 ...................
 
@@ -236,6 +229,11 @@ Changelog
 :mod:`sklearn.linear_model`
 ...........................
 
+- |Fix| :class:`linear_model.LassoCV` and :class:`linear_model.ElasticNetCV` now
+  take sample weights into accounts to define the search grid for the internally tuned
+  `alpha` hyper-parameter. :pr:`29442` by :user:`John Hopfensperger <s-banach> and
+  :user:`Shruti Nath <snath-xoc>`.
+
 - |API| Deprecates `copy_X` in :class:`linear_model.TheilSenRegressor` as the parameter
   has no effect. `copy_X` will be removed in 1.8.
   :pr:`29105` by :user:`Adam Li <adam2392>`.
diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index f94f9495d5a76..0c1421ebffe89 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -156,8 +156,7 @@ def _alpha_grid(
         )
         if sample_weight is not None:
             if y.ndim > 1:
-                yw = y * np.broadcast_to(sample_weight.reshape(-1, 1), y.shape)
-
+                yw = y * sample_weight.reshape(-1, 1)
             else:
                 yw = y * sample_weight
         else:
diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 501eacc2d24d1..52188d1959a55 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1369,7 +1369,6 @@ def test_enet_cv_sample_weight_correctness(
     )
     reg_with_repetitions.fit(X_with_repetitions, y_with_repetitions)
 
-    # ensure that we chose meaningful alphas, i.e. not boundaries
     assert_allclose(reg_with_weights.mse_path_, reg_with_repetitions.mse_path_)
     assert_allclose(reg_with_weights.alphas_, reg_with_repetitions.alphas_)
     assert reg_with_weights.alpha_ == pytest.approx(reg_with_repetitions.alpha_)

From e6c89fe96bc5a7b71c4b1e1af18c757365767d1d Mon Sep 17 00:00:00 2001
From: Shruti Nath <shrutinath@yahoo.com>
Date: Fri, 6 Sep 2024 18:12:34 +0200
Subject: [PATCH 31/33] moved fix in changelog to after API

---
 doc/whats_new/v1.6.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
index 790807f4848c7..5d56e534364fd 100644
--- a/doc/whats_new/v1.6.rst
+++ b/doc/whats_new/v1.6.rst
@@ -229,15 +229,15 @@ Changelog
 :mod:`sklearn.linear_model`
 ...........................
 
+- |API| Deprecates `copy_X` in :class:`linear_model.TheilSenRegressor` as the parameter
+  has no effect. `copy_X` will be removed in 1.8.
+  :pr:`29105` by :user:`Adam Li <adam2392>`.
+
 - |Fix| :class:`linear_model.LassoCV` and :class:`linear_model.ElasticNetCV` now
   take sample weights into accounts to define the search grid for the internally tuned
   `alpha` hyper-parameter. :pr:`29442` by :user:`John Hopfensperger <s-banach> and
   :user:`Shruti Nath <snath-xoc>`.
 
-- |API| Deprecates `copy_X` in :class:`linear_model.TheilSenRegressor` as the parameter
-  has no effect. `copy_X` will be removed in 1.8.
-  :pr:`29105` by :user:`Adam Li <adam2392>`.
-
 :mod:`sklearn.manifold`
 .......................
 

From 2b99c9b2824896b6e0989aed250aa2c61e2d5ca1 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Mon, 9 Sep 2024 11:06:04 +0200
Subject: [PATCH 32/33] Restore non-degenerate alpha check in test

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 52188d1959a55..8f1f1fc0b87d7 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1369,9 +1369,18 @@ def test_enet_cv_sample_weight_correctness(
     )
     reg_with_repetitions.fit(X_with_repetitions, y_with_repetitions)
 
+    # Check that the alpha selection process is the same:
     assert_allclose(reg_with_weights.mse_path_, reg_with_repetitions.mse_path_)
     assert_allclose(reg_with_weights.alphas_, reg_with_repetitions.alphas_)
     assert reg_with_weights.alpha_ == pytest.approx(reg_with_repetitions.alpha_)
+
+    # Ensure that the models selected a non-boundary alpha to make sure that
+    # the sample_weight equivalence test is non-degenerate.
+    alphas = reg_with_weights.alphas_
+    alpha = reg_with_weights.alpha_
+    assert alphas[0] > alpha > alphas[-1]
+
+    # Check that the final model coefficients are the same:
     assert_allclose(reg_with_weights.coef_, reg_with_repetitions.coef_, atol=1e-10)
     assert reg_with_weights.intercept_ == pytest.approx(reg_with_repetitions.intercept_)
 

From 080c8469d2c14766b739da4bcdfd26eddd611c25 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Mon, 9 Sep 2024 11:58:52 +0200
Subject: [PATCH 33/33] Remove alpha boundary check

---
 sklearn/linear_model/tests/test_coordinate_descent.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
index 8f1f1fc0b87d7..c12edf3f0a64c 100644
--- a/sklearn/linear_model/tests/test_coordinate_descent.py
+++ b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1374,12 +1374,6 @@ def test_enet_cv_sample_weight_correctness(
     assert_allclose(reg_with_weights.alphas_, reg_with_repetitions.alphas_)
     assert reg_with_weights.alpha_ == pytest.approx(reg_with_repetitions.alpha_)
 
-    # Ensure that the models selected a non-boundary alpha to make sure that
-    # the sample_weight equivalence test is non-degenerate.
-    alphas = reg_with_weights.alphas_
-    alpha = reg_with_weights.alpha_
-    assert alphas[0] > alpha > alphas[-1]
-
     # Check that the final model coefficients are the same:
     assert_allclose(reg_with_weights.coef_, reg_with_repetitions.coef_, atol=1e-10)
     assert reg_with_weights.intercept_ == pytest.approx(reg_with_repetitions.intercept_)