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Allowing sparse inputs for prediction in AffinityPropagation #20117

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Merged
merged 6 commits into from
May 27, 2021
Merged

Allowing sparse inputs for prediction in AffinityPropagation #20117

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7e6271e
relaxing dense validation
venkyyuvy May 20, 2021
9daac92
linting fix
venkyyuvy May 20, 2021
dae03d1
correcting issue number
venkyyuvy May 20, 2021
66f02b2
changelog
venkyyuvy May 20, 2021
828e058
incorporating review comments
venkyyuvy May 21, 2021
77e5c2e
Update doc/whats_new/v1.0.rst
ogrisel May 27, 2021
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5 changes: 5 additions & 0 deletions doc/whats_new/v1.0.rst
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Expand Up @@ -150,6 +150,11 @@ Changelog
- |Efficiency| :class:`cluster.MiniBatchKMeans` is now faster in multicore
settings. :pr:`17622` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Enhancement| The `predict` and `fit_predict` methods of
:class:`cluster.AffinityPropagation` now accept sparse data type for input
data.
:pr:`20117` by :user:`Venkatachalam Natchiappan <venkyyuvy>`

- |Fix| Fixed a bug in :class:`cluster.MiniBatchKMeans` where the sample
weights were partially ignored when the input is sparse. :pr:`17622` by
:user:`Jérémie du Boisberranger <jeremiedbb>`.
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2 changes: 1 addition & 1 deletion sklearn/cluster/_affinity_propagation.py
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Expand Up @@ -436,7 +436,7 @@ def predict(self, X):
Cluster labels.
"""
check_is_fitted(self)
X = self._validate_data(X, reset=False)
X = self._validate_data(X, reset=False, accept_sparse='csr')
if not hasattr(self, "cluster_centers_"):
raise ValueError("Predict method is not supported when "
"affinity='precomputed'.")
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19 changes: 19 additions & 0 deletions sklearn/cluster/tests/test_affinity_propagation.py
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Expand Up @@ -238,6 +238,25 @@ def test_affinity_propagation_float32():
assert_array_equal(afp.labels_, expected)


def test_sparse_input_for_predict():
# Test to make sure sparse inputs are accepted for predict
# (non-regression test for issue #20049)
af = AffinityPropagation(affinity="euclidean", random_state=42)
af.fit(X)
labels = af.predict(csr_matrix((2, 2)))
assert_array_equal(labels, (2, 2))


def test_sparse_input_for_fit_predict():
# Test to make sure sparse inputs are accepted for fit_predict
# (non-regression test for issue #20049)
af = AffinityPropagation(affinity="euclidean", random_state=42)
rng = np.random.RandomState(42)
X = csr_matrix(rng.randint(0, 2, size=(5, 5)))
labels = af.fit_predict(X)
assert_array_equal(labels, (0, 1, 1, 2, 3))


# TODO: Remove in 1.1
def test_affinity_propagation_pairwise_is_deprecated():
afp = AffinityPropagation(affinity='precomputed')
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