scikit-learn
diff --git a/‎sklearn/preprocessing/data.py
Lines changed: 4 additions & 0 deletions b/‎sklearn/preprocessing/data.py
Lines changed: 4 additions & 0 deletions
diff --git a/‎sklearn/preprocessing/tests/test_data.py
Lines changed: 24 additions & 0 deletions b/‎sklearn/preprocessing/tests/test_data.py
Lines changed: 24 additions & 0 deletions
@@ -1585,6 +1585,10 @@ def transform(self, K, y=None, copy=True):
 
         return K
 
+    @property
+    def _pairwise(self):
+        return True
+
 
 def add_dummy_feature(X, value=1.0):
     """Augment dataset with an additional dummy feature.
 
@@ -52,6 +52,10 @@
 from sklearn.preprocessing.data import PolynomialFeatures
 from sklearn.exceptions import DataConversionWarning
 
+from sklearn.pipeline import Pipeline
+from sklearn.cross_validation import cross_val_predict
+from sklearn.svm import SVR
+
 from sklearn import datasets
 
 iris = datasets.load_iris()
@@ -1370,6 +1374,26 @@ def test_center_kernel():
     assert_array_almost_equal(K_pred_centered, K_pred_centered2)
 
 
+def test_cv_pipeline_precomputed():
+    """Cross-validate a regression on four coplanar points with the same
+    value. Use precomputed kernel to ensure Pipeline with KernelCenterer
+    is treated as a _pairwise operation."""
+    X = np.array([[3, 0, 0], [0, 3, 0], [0, 0, 3], [1, 1, 1]])
+    y_true = np.ones((4,))
+    K = X.dot(X.T)
+    kcent = KernelCenterer()
+    pipeline = Pipeline([("kernel_centerer", kcent), ("svr", SVR())])
+
+    # did the pipeline set the _pairwise attribute?
+    assert_true(pipeline._pairwise)
+
+    # test cross-validation, score should be almost perfect
+    # NB: this test is pretty vacuous -- it's mainly to test integration
+    #     of Pipeline and KernelCenterer
+    y_pred = cross_val_predict(pipeline, K, y_true, cv=4)
+    assert_array_almost_equal(y_true, y_pred)
+
+
 def test_fit_transform():
     rng = np.random.RandomState(0)
     X = rng.random_sample((5, 4))