skbio.tree.TreeNode.compare_tip_distances#

TreeNode.compare_tip_distances(other, sample=None, dist_f=None, shuffle_f=None)[source]#

Compare self to other using tip-to-tip distance matrices.

Parameters:

otherTreeNode: The tree to compare.
sampleint, optional: Randomly subsample this number of tips in common between the trees to compare. This is useful when comparing very large trees.
dist_fcallable, optional: The distance function used to compare two the tip-tip distance matrices. Default is \((1-r)/2\), where \(r\) is the Pearson correlation coefficient between the two matrices.
shuffle_fint, np.random.Generator or callable, optional: The shuffling function used if sample is specified. Default is the shuffle method of a NumPy random generator. If an integer is provided, a random generator will be constructed using this number as the seed.

Changed in version 0.6.3: Switched to NumPy’s new random generator. Can accept a random seed or random generator instance.

Returns:

float: The distance between the trees.

Raises:

ValueError: If there does not exist common tips between the trees.

See also

compare_subsets
compare_rfd
scipy.spatial.distance.correlation

Notes

This method automatically strips out the names that do not match. This is necessary for this method because the distance between non-matching names and matching names is undefined in the tree where they don’t match, and because one needs to reorder the names in the two trees to match up the distance matrices.

Examples

Calculate the distance between two trees. Note that only three taxa are shared between the trees.

>>> from skbio import TreeNode
>>> tree1 = TreeNode.read(["((a:1,b:1):2,(c:0.5,X:0.7):3);"])
>>> tree2 = TreeNode.read(["(((a:1,b:1,Y:1):2,c:3):1,Z:4);"])
>>> dist = tree1.compare_tip_distances(tree2)
>>> print("%.9f" % dist)
0.000133446