`scipy.cluster.hierarchy._hierarchy_impl:optimal_leaf_ordering`

Signature

def optimal_leaf_ordering(Z, y, metric = euclidean)

Given a linkage matrix Z and distance, reorder the cut tree.

Z : ndarray: The hierarchical clustering encoded as a linkage matrix. See linkage for more information on the return structure and algorithm.
y : ndarray: The condensed distance matrix from which Z was generated. Alternatively, a collection of m observation vectors in n dimensions may be passed as an m by n array.
metric : str or function, optional: The distance metric to use in the case that y is a collection of observation vectors; ignored otherwise. See the pdist function for a list of valid distance metrics. A custom distance function can also be used.

Z_ordered : ndarray: A copy of the linkage matrix Z, reordered to minimize the distance between adjacent leaves.

import numpy as np
from scipy.cluster import hierarchy
rng = np.random.default_rng()
X = rng.standard_normal((10, 10))
Z = hierarchy.ward(X)

✓

hierarchy.leaves_list(Z)
hierarchy.leaves_list(hierarchy.optimal_leaf_ordering(Z, X))

✗

array([6, 7, 1, 2, 5, 8, 3, 4, 0, 9], dtype=int32)