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Fix check_array dtype in MinibatchKMeans.partial_fit #14323

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merged 4 commits into from
Jul 15, 2019
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Fix check_array dtype in MinibatchKMeans.partial_fit #14323

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d92bdd3
Fix check_array dtype in MinibatchKMeans.partial_fit
rth Jul 13, 2019
c71dc3c
Improve dtype check
rth Jul 13, 2019
08c4292
Fix docstring
rth Jul 13, 2019
95e96ed
Fix cluster centroids values
rth Jul 13, 2019
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7 changes: 4 additions & 3 deletions sklearn/cluster/k_means_.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1422,8 +1422,8 @@ class MiniBatchKMeans(KMeans):
>>> kmeans = kmeans.partial_fit(X[0:6,:])
>>> kmeans = kmeans.partial_fit(X[6:12,:])
>>> kmeans.cluster_centers_
array([[1, 1],
[3, 4]])
array([[2. , 1. ],
[3.5, 4.5]])
>>> kmeans.predict([[0, 0], [4, 4]])
array([0, 1], dtype=int32)
>>> # fit on the whole data
Expand Down Expand Up @@ -1667,7 +1667,8 @@ def partial_fit(self, X, y=None, sample_weight=None):

"""

X = check_array(X, accept_sparse="csr", order="C")
X = check_array(X, accept_sparse="csr", order="C",
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Do we need to make a copy in order to compute the centers in float? It's a bit weird but I guess it makes sense?
We could do sum on the ints and then divide to get float centers but that might be excessive?

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Do we need to make a copy in order to compute the centers in float? It's a bit weird but I guess it makes sense?

That and the inertia I think.

We could do sum on the ints and then divide to get float centers but that might be excessive?

The larger problem is that a number of intermediary arrays are created in Kmeans/MinibatchKmeans with dtype=X.dtype (presumably to keep float32 / float64 unchanged),

$ rg "dtype=X.dtype" sklearn/cluster/k_means_.py
79:    centers = np.empty((n_clusters, n_features), dtype=X.dtype)
171:        return np.ones(n_samples, dtype=X.dtype)
331:        init = check_array(init, dtype=X.dtype.type, copy=True)
533:    distances = np.zeros(shape=(X.shape[0],), dtype=X.dtype)
670:        distances = np.zeros(shape=(0,), dtype=X.dtype)
751:        centers = np.array(init, dtype=X.dtype)
754:        centers = np.asarray(centers, dtype=X.dtype)
1499:            self.init = np.ascontiguousarray(self.init, dtype=X.dtype)
1516:            old_center_buffer = np.zeros(n_features, dtype=X.dtype)
1521:            old_center_buffer = np.zeros(0, dtype=X.dtype)
1523:        distances = np.zeros(self.batch_size, dtype=X.dtype)
1673:            self.init = np.ascontiguousarray(self.init, dtype=X.dtype)
1702:            distances = np.zeros(X.shape[0], dtype=X.dtype)
1706:                         np.zeros(0, dtype=X.dtype), 0,

sure in each of those we could do dtype conversions as necessary, depending on X.dtype but it would add complexity for, I think, not that common use case. A have not benchmarked it, but my guess is that a copy to float would be negligible with respect to the total run time, and that's what MinibatchKMeans.fit does already.

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ok, seems reasonable.

dtype=[np.float64, np.float32])
n_samples, n_features = X.shape
if hasattr(self.init, '__array__'):
self.init = np.ascontiguousarray(self.init, dtype=X.dtype)
Expand Down
8 changes: 8 additions & 0 deletions sklearn/cluster/tests/test_k_means.py
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Expand Up @@ -942,3 +942,11 @@ def test_k_means_empty_cluster_relocated():

assert len(set(km.labels_)) == 2
assert_allclose(km.cluster_centers_, [[-1], [1]])


def test_minibatch_kmeans_partial_fit_int_data():
# Issue GH #14314
X = np.array([[-1], [1]], dtype=np.int)
km = MiniBatchKMeans(n_clusters=2)
km.partial_fit(X)
assert km.cluster_centers_.dtype.kind == "f"