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[MRG+1] break the tie in Meanshift in case cluster intensities are the same #11901

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Merged
merged 7 commits into from
Sep 5, 2018
Merged

[MRG+1] break the tie in Meanshift in case cluster intensities are the same #11901

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1f4d08d
make meanshift deterministic in case of same intensities
Aug 23, 2018
4d594c7
new line
Aug 23, 2018
1c65db4
fix example
Aug 23, 2018
2d4f76c
fix PEP8
Aug 23, 2018
32c9b44
test the case where intensities are a tie
Aug 23, 2018
0bef05c
new line, ugh
Aug 23, 2018
aa3ebe3
add what's new entries
Aug 23, 2018
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6 changes: 6 additions & 0 deletions doc/whats_new/v0.20.rst
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Expand Up @@ -61,6 +61,7 @@ parameters, may produce different models from the previous version. This often
occurs due to changes in the modelling logic (bug fixes or enhancements), or in
random sampling procedures.

- :class:`cluster.MeanShift` (bug fix)
- :class:`decomposition.IncrementalPCA` in Python 2 (bug fix)
- :class:`decomposition.SparsePCA` (bug fix)
- :class:`ensemble.GradientBoostingClassifier` (bug fix affecting feature importances)
Expand Down Expand Up @@ -140,6 +141,11 @@ Support for Python 3.3 has been officially dropped.
`n_iter_` attribute in the docstring of :class:`cluster.KMeans`.
:issue:`11353` by :user:`Jeremie du Boisberranger <jeremiedbb>`.

- |Fix| Fixed a bug in :func:`cluster.mean_shift` where the assigned labels
were not deterministic if there were multiple clusters with the same
intensities.
:issue:`11901` by :user:`Adrin Jalali <adrinjalali>`.

- |API| Deprecate ``pooling_func`` unused parameter in
:class:`cluster.AgglomerativeClustering`.
:issue:`9875` by :user:`Kumar Ashutosh <thechargedneutron>`.
Expand Down
8 changes: 5 additions & 3 deletions sklearn/cluster/mean_shift_.py
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Expand Up @@ -215,8 +215,10 @@ def mean_shift(X, bandwidth=None, seeds=None, bin_seeding=False,
# If the distance between two kernels is less than the bandwidth,
# then we have to remove one because it is a duplicate. Remove the
# one with fewer points.

sorted_by_intensity = sorted(center_intensity_dict.items(),
key=lambda tup: tup[1], reverse=True)
key=lambda tup: (tup[1], tup[0]),
reverse=True)
sorted_centers = np.array([tup[0] for tup in sorted_by_intensity])
unique = np.ones(len(sorted_centers), dtype=np.bool)
nbrs = NearestNeighbors(radius=bandwidth,
Expand Down Expand Up @@ -359,9 +361,9 @@ class MeanShift(BaseEstimator, ClusterMixin):
... [4, 7], [3, 5], [3, 6]])
>>> clustering = MeanShift(bandwidth=2).fit(X)
>>> clustering.labels_
array([0, 0, 0, 1, 1, 1])
array([1, 1, 1, 0, 0, 0])
>>> clustering.predict([[0, 0], [5, 5]])
array([0, 1])
array([1, 0])
>>> clustering # doctest: +NORMALIZE_WHITESPACE
MeanShift(bandwidth=2, bin_seeding=False, cluster_all=True, min_bin_freq=1,
n_jobs=None, seeds=None)
Expand Down
12 changes: 12 additions & 0 deletions sklearn/cluster/tests/test_mean_shift.py
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Expand Up @@ -101,6 +101,18 @@ def test_unfitted():
assert_false(hasattr(ms, "labels_"))


def test_cluster_intensity_tie():
X = np.array([[1, 1], [2, 1], [1, 0],
[4, 7], [3, 5], [3, 6]])
c1 = MeanShift(bandwidth=2).fit(X)

X = np.array([[4, 7], [3, 5], [3, 6],
[1, 1], [2, 1], [1, 0]])
c2 = MeanShift(bandwidth=2).fit(X)
assert_array_equal(c1.labels_, [1, 1, 1, 0, 0, 0])
assert_array_equal(c2.labels_, [0, 0, 0, 1, 1, 1])


def test_bin_seeds():
# Test the bin seeding technique which can be used in the mean shift
# algorithm
Expand Down