DADApy: Distance-based Analysis of DAta-manifolds in Python

Abstract

DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for comparing different distance metrics. We review the main functionalities of the package and exemplify its usage in toy cases and in a real-world application. DADApy is freely available under the open-source Apache 2.0 license.

arXiv comment9 pages, 6 figures. Patterns (2022)

References in corpus (13)

• Scikit-learn: Machine Learning in Python
• Machine learning and the physical sciences
• PLUMED: a portable plugin for free-energy calculations with molecular dynamics
• Estimating the intrinsic dimension of datasets by a minimal neighborhood information
• Intrinsic dimension of data representations in deep neural networks
• Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering
• Ranking the information content of distance measures
• Unfolding Hidden Barriers by Active Enhanced Sampling
• Exploring the robust extrapolation of high-dimensional machine learning potentials
• The mechanism of RNA base fraying: molecular dynamics simulations analyzed with core-set Markov state models
• Intrinsic dimension estimation for discrete metrics
• Statistically Unbiased Free Energy Estimates from Biased Simulations
• Distributional Results for Model-Based Intrinsic Dimension Estimators

Cited by in corpus (13)

• Ranking the information content of distance measures
• Exploring the robust extrapolation of high-dimensional machine learning potentials
• Intrinsic dimension estimation for discrete metrics
• Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance
• Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks
• Compressing and forecasting atomic material simulations with descriptors
• A Fuzzy Classification Framework to Identify Equivalent Atoms in Complex Materials and Molecules
• Spectral Operator Representations
• Linear scaling causal discovery from high-dimensional time series by dynamical community detection
• Unsupervised detection of semantic correlations in big data
• Density Estimation via Binless Multidimensional Integration
• Family-Vicsek universality of the binary intrinsic dimension of nonequilibrium data
• MDIntrinsicDimension: Dimensionality-Based Analysis of Collective Motions in Macromolecules from Molecular Dynamics Trajectories