message: Make the order of atol centers a bit more reproducible.
diff: @@ -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)
  