`scipy.cluster.hierarchy._hierarchy_impl:optimal_leaf_ordering`

source: /scipy/cluster/hierarchy/_hierarchy_impl.py:1402

Signature

def optimal_leaf_ordering(Z, y, metric = euclidean)

Summary

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

Parameters

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.

Returns

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

Notes

Array API Standard Support

optimal_leaf_ordering has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ⛔                   
PyTorch               ✅                     ⛔                   
JAX                   ✅                     ⛔                   
Dask                  ⚠️ merges chunks      n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

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