def test_sparse_scikit_vs_scipy():
# Test scikit linkage with full connectivity (i.e. unstructured) vs scipy
n, p, k = 10, 5, 3
rng = np.random.RandomState(0)
# Not using a lil_matrix here, just to check that non sparse
# matrices are well handled
connectivity = np.ones((n, n))
for linkage in _TREE_BUILDERS.keys():
for i in range(5):
X = .1 * rng.normal(size=(n, p))
X -= 4. * np.arange(n)[:, np.newaxis]
X -= X.mean(axis=1)[:, np.newaxis]
out = hierarchy.linkage(X, method=linkage)
children_ = out[:, :2].astype(np.int, copy=False)
children, _, n_leaves, _ = _TREE_BUILDERS[linkage](X, connectivity)
# Sort the order of child nodes per row for consistency
children.sort(axis=1)
assert_array_equal(
children, children_, 'linkage tree differs'
' from scipy impl for'
' linkage: ' + linkage)
cut = _hc_cut(k, children, n_leaves)
cut_ = _hc_cut(k, children_, n_leaves)
assess_same_labelling(cut, cut_)
# Test error management in _hc_cut
with pytest.raises(ValueError):
_hc_cut(n_leaves + 1, children, n_leaves)
def _build_tree(self, X):
memory = check_memory(self.memory)
if self.linkage == "ward" and self.affinity != "euclidean":
raise ValueError("{} was provided as affinity. Ward can only work"
"with Euclidean distances.".format(self.affinity))
if self.linkage not in _TREE_BUILDERS:
raise ValueError(
"Unknown linkage type {}. Valid options are {}".format(
self.linkage, _TREE_BUILDERS.keys()))
tree_builder = _TREE_BUILDERS[self.linkage]
# Construct the tree
kwargs = {}
if self.linkage != 'ward':
kwargs['linkage'] = self.linkage
kwargs['affinity'] = self.affinity
out = memory.cache(tree_builder)(X,
n_clusters=None,
return_distance=True,
**kwargs)
# Scikit-learn's tree_builder returns a tuple (children,
# n_connected_components, n_leaves, parent, distances)
self.children_, _, self.n_leaves_, _, self.distances_ = out
