fast-plscan 0.1.0.post1

A fast multi-core implementation of the PLSCAN clustering algorithm.

Persistent Leaves Spatial Clustering for Applications with Noise

This library provides a new clustering algorithm based on HDBSCAN*. The primary advantages of PLSCAN over the hdbscan and fast_hdbscan libraries are:

• PLSCAN automatically finds the optimal minimum cluster size.
• PLSCAN can easily use all available cores to speed up computation.
• PLSCAN has much faster implementations of tree condensing and cluster extraction.
• PLSCAN does not rely on JIT compilation.

To use PLSCAN, you only need to set the min_samples parameter. This parameter controls how many neighbors are considered when measuring distances between points. Setting a higher value for min_samples makes the algorithm group points into larger, smoother clusters, and usually results in fewer, more stable clusters.

import numpy as np
import matplotlib.pyplot as plt

from fast_plscan import PLSCAN

data = np.load("docs/data/data.npy")

clusterer = PLSCAN(
  min_samples = 5, # same as in HDBSCAN
).fit(data)

plt.figure()
plt.scatter(
  *data.T, c=clusterer.labels_ % 10, s=5, alpha=0.5, 
  edgecolor="none", cmap="tab10", vmin=0, vmax=9
)
plt.axis("off")
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
plt.show()

The algorithm creates a hierarchy of leaf-clusters by changing the minimum cluster size. As this parameter varies, clusters appear or disappear. For each minimum cluster size, the algorithm measures how long these leaf-clusters persist. It then selects the minimum cluster size where the total persistence is highest, giving the most stable clustering. You can visualize this hierarchy using the leaf_tree_ attribute, which provides an alternative to HDBSCAN*'s condensed cluster tree.

clusterer.leaf_tree_.plot(leaf_separation=0.1)
plt.show()

You can also explore how the clustering changes for other important values of the minimum cluster size. The cluster_layers method automatically finds the most persistent clusterings and returns their cluster labels and membership strengths.

layers = clusterer.cluster_layers(max_peaks=4)
for i, (size, labels, probs) in enumerate(layers):
  plt.subplot(2, 2, i + 1)
  plt.scatter(
    *data.T,
    c=labels % 10,
    alpha=np.maximum(0.1, probs),
    s=1,
    linewidth=0,
    cmap="tab10",
  )
  plt.title(f"min_cluster_size={int(size)}")
  plt.axis("off")
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
plt.show()

Local development

The development workflow works best by pre-installing python dependencies with pip (or alternatives):

pip install numpy scipy matplotlib scikit-learn scikit-build-core nanobind setuptools_scm

Building the package requires cmake and a C++ 23 compiler with OpenMP support. The OpenMP version must support user-defined reductions. Selecting the proper OpenMP version requires some additional configuration, see below. Assuming the compiler and OpenMP are present, the package can be compiled and installed with:

pip install --no-deps --no-build-isolation -ve .

To change the build type, add -C cmake.build-type=Debug or -C cmake.build-type=Release to the command.

scikit-build-core also experimentally supports editable installs (see their documentation):

pip install --no-deps --no-build-isolation -C editable.rebuild=true -ve .

Linux

It may be necessary to tell cmake which compiler it should use. For example, using g++-14 when that is not the system default can be done by adding a -C cmake.args="-DCMAKE_CXX_COMPILER=g++-14" option. The -C cmake.args=... option does not have to be repeated on rebuilds.

MacOS

MacOS requires installing OpenMP using homebrew:

brew install libomp

Also update the ~/.zshrc config file with:

export OpenMP_ROOT=$(brew --prefix)/opt/libomp

or pass OpenMP_ROOT as cmake argument:

pip install --no-deps --no-build-isolation \
  -C cmake.args="-DOpenMP_ROOT=$(brew --prefix)/opt/libomp" \
  -ve .

Windows

The default MSVC C++ compiler on windows does not support a recent enough OpenMP. In addition, the default powershell terminal on windows is not configured for cmake to find the correct OpenMP version. Instead, use a developer powershell configured for a 64-bit target architecture. To open such a terminal, run the following code in a normal Powershell terminal:

$vswhere = "${env:ProgramFiles(x86)}/Microsoft Visual Studio/Installer/vswhere.exe"
$iloc = & $vswhere -products * -latest -property installationpath
$devddl = "$iloc/Common7/Tools/Microsoft.VisualStudio.DevShell.dll"
Import-Module $devddl; Enter-VsDevShell -Arch amd64 -VsInstallPath $iloc -SkipAutomaticLocation

In addition, select the MSVC Clang compiler using -C cmake.args="-T ClangCL" the first time the package is installed:

pip install --no-deps --no-build-isolation -C cmake.args="-T ClangCL" -ve . 

The -C cmake.args=... option does not have to be repeated on rebuilds.

You may need to install the visual studio build tools with the optional Clang compiler support enabled.

Citing

When using this work, please cite our (upcoming) preprint:

@article{bot2025plscan,
  title         = {Persistent Multiscale Density-based Clustering},
  author        = {Dani{\"{e}}l M. Bot and Leland McInnes and Jan Aerts},
  year          = {2025},
  month         = {12},
  archiveprefix = {arXiv},
  eprint        = {TODO},
  primaryclass  = {cs.CL}
}

Licensing

The fast-plscan package has a 3-Clause BSD license.