Distance-based Analysis of DAta-manifolds in python
DADApy is a Python package for the characterization of manifolds in high-dimensional spaces.
For more details and tutorials, visit the homepage at: https://dadapy.readthedocs.io/
import numpy as np
from dadapy.data import Data
# Generate a simple 3D gaussian dataset
X = np.random.normal(0, 1, (1000, 3))
# initialize the "Data" class with the set of coordinates
data = Data(X)
# compute distances up to the 100th nearest neighbor
data.compute_distances(maxk=100)
# compute the intrinsic dimension using 2nn estimator
id, id_error, id_distance = data.compute_id_2NN()
# compute the intrinsic dimension up to the 64th nearest neighbors using Gride
id_list, id_error_list, id_distance_list = data.return_id_scaling_gride(range_max=64)
# compute the density using PAk, a point adaptive kNN estimator
log_den, log_den_error = data.compute_density_PAk()
# find the peaks of the density profile through the ADP algorithm
cluster_assignment = data.compute_clustering_ADP()
# compute the neighborhood overlap with another dataset
X2 = np.random.normal(0, 1, (1000, 5))
overlap_x2 = data.return_data_overlap(X2)
# compute the neighborhood overlap with a set of labels
labels = np.repeat(np.arange(10), 100)
overlap_labels = data.return_label_overlap(labels)
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)
kNN estimator
k*NN estimator (kNN with an adaptive choice of k)
PAk estimator
Rodriguez et al., JCTC (2018)
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)
Neighbourhood overlap
Doimo et al., NeurIPS (2020)
Information imbalance
Glielmo et al., PNAS Nexus (2022)
Differentiable Information Imbalance
The package is compatible with the Python versions 3.7, 3.8, 3.9, 3.10, 3.11, and 3.12. 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 visualizations.
The package contains Cython-generated C extensions that are automatically compiled during installation.
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:
pip install git+https://github.com/sissa-data-science/DADApy
Alternatively, if you'd like to modify the implementation of some function locally you can download the repository and install the package with:
git clone https://github.com/sissa-data-science/DADApy.git
cd DADApy
python setup.py build_ext --inplace
pip install .
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},
}