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[MRG+1] Make KernelCenterer a _pairwise operation #6900

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Merged
merged 4 commits into from
Jun 22, 2016
Merged

[MRG+1] Make KernelCenterer a _pairwise operation #6900

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2242b1b
Make KernelCenterer a _pairwise operation
fishcorn Jun 17, 2016
cc9dbac
Adding test for PR #6900
fishcorn Jun 17, 2016
13f68c9
Simplifying imports and test
fishcorn Jun 17, 2016
055bc4c
pep8
fishcorn Jun 22, 2016
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4 changes: 4 additions & 0 deletions sklearn/preprocessing/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.
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24 changes: 24 additions & 0 deletions sklearn/preprocessing/tests/test_data.py
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Expand Up @@ -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()
Expand Down Expand Up @@ -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)
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Could you put cv=2, to reduce computing power.

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Forget it. I'll merge and do it.

assert_array_almost_equal(y_true, y_pred)
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please run a pep8 checker on this code and ping us when done



def test_fit_transform():
rng = np.random.RandomState(0)
X = rng.random_sample((5, 4))
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