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scipy.cluster.hierarchy.

# to_tree

scipy.cluster.hierarchy.to_tree(Z, rd=False)

  Convert a linkage matrix into an easy-to-use tree object.

  The reference to the root `ClusterNode` object is returned (by default).

  Each `ClusterNode` object has a `left`, `right`, `dist`, `id`, and `count` attribute. The left and right attributes point to ClusterNode objects that were combined to generate the cluster. If both are None then the `ClusterNode` object is a leaf node, its count must be 1, and its distance is meaningless but set to 0.

  Note: This function is provided for the convenience of the library user. ClusterNodes are not used as input to any of the functions in this library.

  Parameters:
    Zndarray

      The linkage matrix in proper form (see the `linkage` function documentation).

    rdbool, optional

      When False (default), a reference to the root `ClusterNode` object is returned. Otherwise, a tuple `(r, d)` is returned. `r` is a reference to the root node while `d` is a list of `ClusterNode` objects - one per original entry in the linkage matrix plus entries for all clustering steps. If a cluster id is less than the number of samples `n` in the data that the linkage matrix describes, then it corresponds to a singleton cluster (leaf node). See `linkage` for more information on the assignment of cluster ids to clusters.

  Returns:
    treeClusterNode or tuple (ClusterNode, list of ClusterNode)

      If `rd` is False, a `ClusterNode`. If `rd` is True, a list of length `2*n - 1`, with `n` the number of samples. See the description of rd above for more details.

  See also

  `linkage`, `is_valid_linkage`, `ClusterNode`

  Notes

  `to_tree` 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 | ⚠️ no JIT | ⚠️ no JIT
  Dask | ⚠️ computes graph | n/a

  See Support for the array API standard for more information.

  Examples

      >>> import numpy as np
      >>> from scipy.cluster import hierarchy
      >>> rng = np.random.default_rng()
      >>> x = rng.random((5, 2))
      >>> Z = hierarchy.linkage(x)
      >>> hierarchy.to_tree(Z)
      <scipy.cluster.hierarchy.ClusterNode object at ...
      >>> rootnode, nodelist = hierarchy.to_tree(Z, rd=True)
      >>> rootnode
      <scipy.cluster.hierarchy.ClusterNode object at ...
      >>> len(nodelist)
      9