Merge pull request scikit-learn#4838 from trevorstephens/ridge_sw

[MRG+1] Add sample_weight support to RidgeClassifier

Author: amueller

doc/whats_new.rst

@@ -71,6 +71,8 @@ Enhancements
      It is now possible to ignore one or more labels, such as where
      a multiclass problem has a majority class to ignore. By `Joel Nothman`_.
 
+   - Add ``sample_weight`` support to :class:`linear_model.RidgeClassifier`.
+     By `Trevor Stephens`_.
 
 Bug fixes
 .........

sklearn/linear_model/ridge.py

@@ -572,7 +572,7 @@ def __init__(self, alpha=1.0, fit_intercept=True, normalize=False,
             copy_X=copy_X, max_iter=max_iter, tol=tol, solver=solver)
         self.class_weight = class_weight
 
-    def fit(self, X, y):
+    def fit(self, X, y, sample_weight=None):
         """Fit Ridge regression model.
 
         Parameters
@@ -583,20 +583,24 @@ def fit(self, X, y):
         y : array-like, shape = [n_samples]
             Target values
 
+        sample_weight : float or numpy array of shape (n_samples,)
+            Sample weight.
+
         Returns
         -------
         self : returns an instance of self.
         """
+        if sample_weight is None:
+            sample_weight = 1.
+
         self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
         Y = self._label_binarizer.fit_transform(y)
         if not self._label_binarizer.y_type_.startswith('multilabel'):
             y = column_or_1d(y, warn=True)
 
-        if self.class_weight:
-            # get the class weight corresponding to each sample
-            sample_weight = compute_sample_weight(self.class_weight, y)
-        else:
-            sample_weight = None
+        # modify the sample weights with the corresponding class weight
+        sample_weight = (sample_weight *
+                         compute_sample_weight(self.class_weight, y))
 
         super(RidgeClassifier, self).fit(X, Y, sample_weight=sample_weight)
         return self

sklearn/linear_model/tests/test_ridge.py

@@ -489,6 +489,35 @@ def test_class_weights():
     assert_array_almost_equal(clf.intercept_, clfa.intercept_)
 
 
+def test_class_weight_vs_sample_weight():
+    """Check class_weights resemble sample_weights behavior."""
+    for clf in (RidgeClassifier, RidgeClassifierCV):
+
+        # Iris is balanced, so no effect expected for using 'balanced' weights
+        clf1 = clf()
+        clf1.fit(iris.data, iris.target)
+        clf2 = clf(class_weight='balanced')
+        clf2.fit(iris.data, iris.target)
+        assert_almost_equal(clf1.coef_, clf2.coef_)
+
+        # Inflate importance of class 1, check against user-defined weights
+        sample_weight = np.ones(iris.target.shape)
+        sample_weight[iris.target == 1] *= 100
+        class_weight = {0: 1., 1: 100., 2: 1.}
+        clf1 = clf()
+        clf1.fit(iris.data, iris.target, sample_weight)
+        clf2 = clf(class_weight=class_weight)
+        clf2.fit(iris.data, iris.target)
+        assert_almost_equal(clf1.coef_, clf2.coef_)
+
+        # Check that sample_weight and class_weight are multiplicative
+        clf1 = clf()
+        clf1.fit(iris.data, iris.target, sample_weight ** 2)
+        clf2 = clf(class_weight=class_weight)
+        clf2.fit(iris.data, iris.target, sample_weight)
+        assert_almost_equal(clf1.coef_, clf2.coef_)
+
+
 def test_class_weights_cv():
     # Test class weights for cross validated ridge classifier.
     X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],