ollyfutur/DADApy

Distance-based Analysis of DAta-manifolds in python

DADApy is a Python package for the characterisation of manifolds in high dimensional spaces.

Homepage

For more details and tutorials, visit the homepage at: https://dadapy.readthedocs.io/

Quick Example

import numpy as np
from dadapy.data import Data

# Generate a simple 3D gaussian dataset
X = np.random.normal(0, 1, (1000, 3))

# initialise the "Data" class with the set of coordinates
data = Data(X)

# compute distances up to the 100th nearest neighbour
data.compute_distances(maxk=100)

# compute the intrinsic dimension using 2nn estimator
data.compute_id_2NN()

# compute the density using PAk, a point adaptive kNN estimator
data.compute_density_PAk()

# find the peaks of the density profile through the ADP algorithm
data.compute_clustering_ADP()

Currently implemented algorithms

• Intrinsic dimension estimators

• Two-NN estimator

  Facco et al., Scientific Reports (2017)

• Gride estimator

  Denti et al., Scientific Reports (2022)

• I3D estimator (for both continuous and discrete spaces)

  Macocco et al., Physical Review Letters (2023)

• Density estimators

• kNN estimator

• k*NN estimator (kNN with adaptive choice of k)

• PAk estimator

  Rodriguez et al., JCTC (2018)

• Density peaks clustering methods

• Density peaks clustering

  Rodriguez and Laio, Science (2014)

• Advanced density peaks clustering

  d’Errico et al., Information Sciences (2021)

• k-peak clustering

  Sormani, Rodriguez and Laio, JCTC (2020)

• Manifold comparison tools

• Neighbourhood overlap

  Doimo et al., NeurIPS (2020)

• Information imbalance

  Glielmo et al., PNAS Nexus (2022)

Installation

The package is compatible with Python >= 3.7 (tested on 3.7, 3.8 and 3.9). We currently only support Unix-based systems, including Linux and macOS. For Windows-machines we suggest using the Windows Subsystem for Linux (WSL).

The package requires numpy, scipy and scikit-learn, and matplotlib for the visualisations.

The package contains Cython-generated C extensions that are automatically compiled during install.

The latest release is available through pip

pip install dadapy

To install the latest development version, clone the source code from github and install it with pip as follows

git clone https://github.com/sissa-data-science/DADApy.git
cd DADApy
pip install .

Citing DADApy

A description of the package is available here.

Please consider citing it if you found this package useful for your research

@article{dadapy,
    title = {DADApy: Distance-based analysis of data-manifolds in Python},
    journal = {Patterns},
    pages = {100589},
    year = {2022},
    issn = {2666-3899},
    doi = {https://doi.org/10.1016/j.patter.2022.100589},
    url = {https://www.sciencedirect.com/science/article/pii/S2666389922002070},
    author = {Aldo Glielmo and Iuri Macocco and Diego Doimo and Matteo Carli and Claudio Zeni and Romina Wild and Maria d’Errico and Alex Rodriguez and Alessandro Laio},
    }