scikit-learn · jnothman · Nov 13, 2017 · Nov 11, 2017 · Nov 13, 2017
diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
@@ -81,6 +81,10 @@ Classifiers and regressors
   ``inverse_func`` are the inverse of each other.
   :issue:`9399` by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- Add `sample_weight` parameter to the fit method of
+  :class:`linear_model.BayesianRidge` for weighted linear regression.
+  :issue:`10111` by :user:`Peter St. John <pstjohn>`.
+
 Model evaluation and meta-estimators
 
 - A scorer based on :func:`metrics.brier_score_loss` is also available.

diff --git a/sklearn/linear_model/bayes.py b/sklearn/linear_model/bayes.py
@@ -11,7 +11,7 @@
 from scipy import linalg
 from scipy.linalg import pinvh
 
-from .base import LinearModel
+from .base import LinearModel, _rescale_data
 from ..base import RegressorMixin
 from ..utils.extmath import fast_logdet
 from ..utils import check_X_y
@@ -140,7 +140,7 @@ def __init__(self, n_iter=300, tol=1.e-3, alpha_1=1.e-6, alpha_2=1.e-6,
         self.copy_X = copy_X
         self.verbose = verbose
 
-    def fit(self, X, y):
+    def fit(self, X, y, sample_weight=None):
         """Fit the model
 
         Parameters
@@ -150,13 +150,25 @@ def fit(self, X, y):
         y : numpy array of shape [n_samples]
             Target values. Will be cast to X's dtype if necessary
 
+        sample_weight : numpy array of shape [n_samples]
+            Individual weights for each sample
+
+            .. versionadded:: 0.20
+               parameter *sample_weight* support to BayesianRidge.
+
         Returns
         -------
         self : returns an instance of self.
         """
         X, y = check_X_y(X, y, dtype=np.float64, y_numeric=True)
         X, y, X_offset_, y_offset_, X_scale_ = self._preprocess_data(
-            X, y, self.fit_intercept, self.normalize, self.copy_X)
+            X, y, self.fit_intercept, self.normalize, self.copy_X,
+            sample_weight=sample_weight)
+
+        if sample_weight is not None:
+            # Sample weight can be implemented via a simple rescaling.
+            X, y = _rescale_data(X, y, sample_weight)
+
         self.X_offset_ = X_offset_
         self.X_scale_ = X_scale_
         n_samples, n_features = X.shape

diff --git a/sklearn/linear_model/tests/test_bayes.py b/sklearn/linear_model/tests/test_bayes.py
@@ -50,6 +50,21 @@ def test_bayesian_ridge_parameter():
     assert_almost_equal(rr_model.intercept_, br_model.intercept_)
 
 
+def test_bayesian_sample_weights():
+    # Test correctness of the sample_weights method
+    X = np.array([[1, 1], [3, 4], [5, 7], [4, 1], [2, 6], [3, 10], [3, 2]])
+    y = np.array([1, 2, 3, 2, 0, 4, 5]).T
+    w = np.array([4, 3, 3, 1, 1, 2, 3]).T
+
+    # A Ridge regression model using an alpha value equal to the ratio of
+    # lambda_ and alpha_ from the Bayesian Ridge model must be identical
+    br_model = BayesianRidge(compute_score=True).fit(X, y, sample_weight=w)
+    rr_model = Ridge(alpha=br_model.lambda_ / br_model.alpha_).fit(
+        X, y, sample_weight=w)
+    assert_array_almost_equal(rr_model.coef_, br_model.coef_)
+    assert_almost_equal(rr_model.intercept_, br_model.intercept_)
+
+
 def test_toy_bayesian_ridge_object():
     # Test BayesianRidge on toy
     X = np.array([[1], [2], [6], [8], [10]])