Make the order of atol centers a bit more reproducible.

1 files changed, 13 insertions, 11 deletions

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index a6d86821..745fe1e5 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -710,17 +710,17 @@ class Atol(BaseEstimator, TransformerMixin):
     >>> b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]])
     >>> c = np.array([[3, 2, -1], [1, 2, -1]])
     >>> atol_vectoriser = Atol(quantiser=KMeans(n_clusters=2, random_state=202006))
-    >>> atol_vectoriser.fit(X=[a, b, c]).centers # doctest: +SKIP
-    array([[ 2.        ,  0.66666667,  3.33333333],
-           [ 2.6       ,  2.8       , -0.4       ]])
-    >>> atol_vectoriser(a) # doctest: +SKIP
-    array([1.18168665, 0.42375966])
-    >>> atol_vectoriser(c) # doctest: +SKIP
-    array([0.02062512, 1.25157463])
-    >>> atol_vectoriser.transform(X=[a, b, c]) # doctest: +SKIP
-    array([[1.18168665, 0.42375966],
-           [0.29861028, 1.06330156],
-           [0.02062512, 1.25157463]])
+    >>> atol_vectoriser.fit(X=[a, b, c]).centers
+    array([[ 2.6       ,  2.8       , -0.4       ],
+           [ 2.        ,  0.66666667,  3.33333333]])
+    >>> atol_vectoriser(a)
+    array([0.42375966, 1.18168665])
+    >>> atol_vectoriser(c)
+    array([1.25157463, 0.02062512])
+    >>> atol_vectoriser.transform(X=[a, b, c])
+    array([[0.42375966, 1.18168665],
+           [1.06330156, 0.29861028],
+           [1.25157463, 0.02062512]])
     """
     # Note the example above must be up to date with the one in tests called test_atol_doc
     def __init__(self, quantiser, weighting_method="cloud", contrast="gaussian"):
@@ -771,6 +771,8 @@ class Atol(BaseEstimator, TransformerMixin):
         measures_concat = np.concatenate(X)
         self.quantiser.fit(X=measures_concat, sample_weight=sample_weight)
         self.centers = self.quantiser.cluster_centers_
+        # Hack, but some people are unhappy if the order depends on the version of sklearn
+        self.centers = self.centers[np.lexsort(self.centers.T)]
         if self.quantiser.n_clusters == 1:
             dist_centers = pairwise.pairwise_distances(measures_concat)
             np.fill_diagonal(dist_centers, 0)