From a8b91cc4716f5f93f9d7f21c641a87ce238ac643 Mon Sep 17 00:00:00 2001
From: Antoine Baker <antoine.baker59@gmail.com>
Date: Fri, 21 Feb 2025 17:53:07 +0100
Subject: [PATCH] mixin to request sample weight

---
 sklearn/base.py                             | 16 +++++++++++++++-
 sklearn/covariance/_empirical_covariance.py |  5 ++++-
 sklearn/linear_model/_coordinate_descent.py |  5 ++++-
 sklearn/metrics/_scorer.py                  | 14 +++++++++++++-
 sklearn/preprocessing/_data.py              |  2 +-
 5 files changed, 37 insertions(+), 5 deletions(-)

diff --git a/sklearn/base.py b/sklearn/base.py
index dabaa93ac29b7..ce51d4e03f3f0 100644
--- a/sklearn/base.py
+++ b/sklearn/base.py
@@ -10,6 +10,7 @@
 import re
 import warnings
 from collections import defaultdict
+from typing import Union
 
 import numpy as np
 
@@ -153,7 +154,20 @@ def _clone_parametrized(estimator, *, safe=True):
     return new_object
 
 
-class BaseEstimator(_HTMLDocumentationLinkMixin, _MetadataRequester):
+class _SampleWeightEstimatorMixin(_MetadataRequester):
+    """A Mixin to request ``sample_weight`` by default.
+
+    This Mixin makes the object to request ``sample_weight`` by default as ``True``
+    for both ``fit`` and ``score`` methods.
+
+    .. versionadded:: 1.7
+    """
+
+    __metadata_request__fit: dict[str, Union[bool, str]] = {"sample_weight": True}
+    __metadata_request__score: dict[str, Union[bool, str]] = {"sample_weight": True}
+
+
+class BaseEstimator(_HTMLDocumentationLinkMixin, _SampleWeightEstimatorMixin):
     """Base class for all estimators in scikit-learn.
 
     Inheriting from this class provides default implementations of:
diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py
index 955046fa37d4b..97985799eada9 100644
--- a/sklearn/covariance/_empirical_covariance.py
+++ b/sklearn/covariance/_empirical_covariance.py
@@ -184,7 +184,10 @@ class EmpiricalCovariance(BaseEstimator):
     """
 
     # X_test should have been called X
-    __metadata_request__score = {"X_test": metadata_routing.UNUSED}
+    __metadata_request__score = {
+        "X_test": metadata_routing.UNUSED,
+        "sample_weight": True,
+    }
 
     _parameter_constraints: dict = {
         "store_precision": ["boolean"],
diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index b98cf08925910..9d92ad8c7a739 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -879,7 +879,10 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
 
     # "check_input" is used for optimisation and isn't something to be passed
     # around in a pipeline.
-    __metadata_request__fit = {"check_input": metadata_routing.UNUSED}
+    __metadata_request__fit = {
+        "check_input": metadata_routing.UNUSED,
+        "sample_weight": True,
+    }
 
     _parameter_constraints: dict = {
         "alpha": [Interval(Real, 0, None, closed="left")],
diff --git a/sklearn/metrics/_scorer.py b/sklearn/metrics/_scorer.py
index 549b868cebe60..0125fd8313924 100644
--- a/sklearn/metrics/_scorer.py
+++ b/sklearn/metrics/_scorer.py
@@ -195,7 +195,19 @@ def get_metadata_routing(self):
         )
 
 
-class _BaseScorer(_MetadataRequester):
+class _SampleWeightScorerMixin(_MetadataRequester):
+    """A Mixin to request ``sample_weight`` by default.
+
+    This Mixin makes the object to request ``sample_weight`` by default as ``True``
+    for the ``score`` method.
+
+    .. versionadded:: 1.7
+    """
+
+    __metadata_request__score = {"sample_weight": True}
+
+
+class _BaseScorer(_SampleWeightScorerMixin):
     """Base scorer that is used as `scorer(estimator, X, y_true)`.
 
     Parameters
diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index f0d1defe61ca9..a0a0eafc857a9 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -2431,7 +2431,7 @@ class KernelCenterer(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEsti
 
     # X is called K in these methods.
     __metadata_request__transform = {"K": metadata_routing.UNUSED}
-    __metadata_request__fit = {"K": metadata_routing.UNUSED}
+    __metadata_request__fit = {"K": metadata_routing.UNUSED, "sample_weight": True}
 
     def fit(self, K, y=None):
         """Fit KernelCenterer.