[MRG+1] break the tie in Meanshift in case cluster intensities are th…

…e same (scikit-learn#11901)

doc/whats_new/v0.20.rst

@@ -63,6 +63,7 @@ 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)
@@ -151,6 +152,11 @@ 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>`.

sklearn/cluster/mean_shift_.py

@@ -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,
@@ -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)

sklearn/cluster/tests/test_mean_shift.py

@@ -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