diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py
index 3acb308cf3b92..dd47030308d40 100644
--- a/sklearn/linear_model/least_angle.py
+++ b/sklearn/linear_model/least_angle.py
@@ -606,7 +606,8 @@ def __init__(self, fit_intercept=True, verbose=False, normalize=True,
         self.copy_X = copy_X
         self.fit_path = fit_path
 
-    def _get_gram(self, precompute, X, y):
+    @staticmethod
+    def _get_gram(precompute, X, y):
         if (not hasattr(precompute, '__array__')) and (
                 (precompute is True) or
                 (precompute == 'auto' and X.shape[0] > X.shape[1]) or
diff --git a/sklearn/linear_model/tests/test_least_angle.py b/sklearn/linear_model/tests/test_least_angle.py
index 8545ecd988399..9c9a883f96383 100644
--- a/sklearn/linear_model/tests/test_least_angle.py
+++ b/sklearn/linear_model/tests/test_least_angle.py
@@ -80,7 +80,6 @@ def test_simple_precomputed():
 
 def test_all_precomputed():
     # Test that lars_path with precomputed Gram and Xy gives the right answer
-    X, y = diabetes.data, diabetes.target
     G = np.dot(X.T, X)
     Xy = np.dot(X.T, y)
     for method in 'lar', 'lasso':
@@ -188,7 +187,6 @@ def test_no_path_all_precomputed():
         [linear_model.Lars, linear_model.LarsCV, linear_model.LassoLarsIC])
 def test_lars_precompute(classifier):
     # Check for different values of precompute
-    X, y = diabetes.data, diabetes.target
     G = np.dot(X.T, X)
 
     clf = classifier(precompute=G)