scikit-learn · lorentzenchr · Dec 22, 2020 · Dec 21, 2020 · Dec 21, 2020 · Dec 22, 2020
diff --git a/doc/whats_new/v1.0.rst b/doc/whats_new/v1.0.rst
@@ -56,6 +56,12 @@ Changelog
  in multicore settings. :pr:`19052` by
  :user:`Yusuke Nagasaka <YusukeNagasaka>`.

+:mod:`sklearn.linear_model`
+...........................
+
+- |Fix| :meth:`ElasticNet.fit` no longer modifies `sample_weight` in place.
+  :pr:`19055` by `Thomas Fan`_.
+
 Code and Documentation Contributors
 -----------------------------------


diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
@@ -790,7 +790,7 @@ def fit(self, X, y, sample_weight=None, check_input=True):
                                                     dtype=X.dtype)
            # simplify things by rescaling sw to sum up to n_samples
            # => np.average(x, weights=sw) = np.mean(sw * x)
-            sample_weight *= (n_samples / np.sum(sample_weight))
+            sample_weight = sample_weight * (n_samples / np.sum(sample_weight))
            # Objective function is:
            # 1/2 * np.average(squared error, weights=sw) + alpha * penalty
            # but coordinate descent minimizes:

diff --git a/sklearn/linear_model/tests/test_coordinate_descent.py b/sklearn/linear_model/tests/test_coordinate_descent.py
@@ -1214,3 +1214,22 @@ def test_linear_models_cv_fit_for_all_backends(backend, estimator):

    with joblib.parallel_backend(backend=backend):
        estimator(n_jobs=2, cv=3).fit(X, y)
+
+
+@pytest.mark.parametrize("check_input", [True, False])
+def test_enet_sample_weight_does_not_overwrite_sample_weight(check_input):
+    """Check that ElasticNet does not overwrite sample_weights."""
+
+    rng = np.random.RandomState(0)
+    n_samples, n_features = 10, 5
+
+    X = rng.rand(n_samples, n_features)
+    y = rng.rand(n_samples)
+
+    sample_weight_1_25 = 1.25 * np.ones_like(y)
+    sample_weight = sample_weight_1_25.copy()
+
+    reg = ElasticNet()
+    reg.fit(X, y, sample_weight=sample_weight, check_input=check_input)
+
+    assert_array_equal(sample_weight, sample_weight_1_25)