Package: gudhi
Version: 3.6.0+dfsg-4
Severity: normal
scipy 1.10 is now available in experimental.
gudhi fails debci tests using it.
We are considering uploading scipy 1.10 to unstable in order to
included it in the forthcoming stable release. If we proceed with
that, then this bug will become Severity: serious.
The errors are TypeError: Unexpected keyword argument {'n_jobs': -1}
A sample from the failing test log is
________________________________ test_tomato_1 _________________________________
def test_tomato_1():
a = [(1, 2), (1.1, 1.9), (0.9, 1.8), (10, 0), (10.1, 0.05), (10.2,
-0.1), (5.4, 0)]
t = Tomato(metric="euclidean", n_clusters=2, k=4, n_jobs=-1, eps=0.05)
> assert np.array_equal(t.fit_predict(a), [1, 1, 1, 0, 0, 0, 0]) # or
> with swapped 0 and 1
test/test_tomato.py:23:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/lib/python3/dist-packages/gudhi/clustering/tomato.py:263: in fit_predict
return self.fit(X, y, weights).labels_
/usr/lib/python3/dist-packages/gudhi/clustering/tomato.py:156: in fit
knn = KNearestNeighbors(return_index=need_knn_ngb,
return_distance=need_knn_dist, **knn_args).fit_transform(
/usr/lib/python3/dist-packages/gudhi/point_cloud/knn.py:86: in fit_transform
return self.fit(X).transform(X)
/usr/lib/python3/dist-packages/gudhi/point_cloud/knn.py:318: in transform
distances, neighbors = self.kdtree.query(X, k=self.k, **qargs)
_ckdtree.pyx:783: in scipy.spatial._ckdtree.cKDTree.query
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E TypeError: Unexpected keyword argument {'n_jobs': -1}
_ckdtree.pyx:387: TypeError
