Difference between scipy.spatial.KDTree and scipy.spatial.cKDTree
Question:
What is the difference between these two algorithms?
Answers:
cKDTree is a subset of KDTree, implemented in C++ wrapped in Cython, so therefore faster.
Each of them is
a binary trie, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value.
but KDTree
also supports all-neighbors queries, both with arrays of points and with other kd-trees. These do use a reasonably efficient algorithm, but the kd-tree is not necessarily the best data structure for this sort of calculation.
In a use case (5D nearest neighbor look ups in a KDTree with approximately 100K points) cKDTree is around 12x faster than KDTree.
Currently, both have almost same APIs, and cKDTree is faster than KDTree.
So, In the near future, SciPy developers are planning to remove KDTree, and cKDTree will be renamed to KDTree in a backwards-compatible way.
Ref:
Detailed SciPy Roadmap — SciPy v1.6.0.dev Reference Guide
https://docs.scipy.org/doc/scipy/reference/roadmap-detailed.html#spatial
Update 2022: cKDTree is deprecated
The current (v1.8) SciPy documentation states that scipy.spatial.cKDTree is now deprecated and was replaced by the functionally-identical scipy.spatial.KDTree.
Here is the note:
cKDTree is functionally identical to KDTree. Prior to SciPy v1.6.0, cKDTree had better performance and slightly different functionality but now the two names exist only for backward-compatibility reasons. If compatibility with SciPy < 1.6 is not a concern, prefer KDTree.
What is the difference between these two algorithms?
cKDTree is a subset of KDTree, implemented in C++ wrapped in Cython, so therefore faster.
Each of them is
a binary trie, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value.
but KDTree
also supports all-neighbors queries, both with arrays of points and with other kd-trees. These do use a reasonably efficient algorithm, but the kd-tree is not necessarily the best data structure for this sort of calculation.
In a use case (5D nearest neighbor look ups in a KDTree with approximately 100K points) cKDTree is around 12x faster than KDTree.
Currently, both have almost same APIs, and cKDTree is faster than KDTree.
So, In the near future, SciPy developers are planning to remove KDTree, and cKDTree will be renamed to KDTree in a backwards-compatible way.
Ref:
Detailed SciPy Roadmap — SciPy v1.6.0.dev Reference Guide
https://docs.scipy.org/doc/scipy/reference/roadmap-detailed.html#spatial
Update 2022: cKDTree is deprecated
The current (v1.8) SciPy documentation states that scipy.spatial.cKDTree is now deprecated and was replaced by the functionally-identical scipy.spatial.KDTree.
Here is the note:
cKDTree is functionally identical to KDTree. Prior to SciPy v1.6.0, cKDTree had better performance and slightly different functionality but now the two names exist only for backward-compatibility reasons. If compatibility with SciPy < 1.6 is not a concern, prefer KDTree.