KeplerMapper是一个用于高维数据和三维点云数据可视化的Python类

KeplerMapper
Nature uses as little as possible of anything. - Johannes Kepler

This is a Python implementation of the TDA Mapper algorithm for visualization of high-dimensional data. For complete documentation, see https://kepler-mapper.scikit-tda.org.

KeplerMapper employs approaches based on the Mapper algorithm (Singh et al.) as first described in the paper “Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition”.

KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms.

Install
Dependencies
KeplerMapper requires:

Python (>= 2.7 or >= 3.3)
NumPy
Scikit-learn
Using the plotly visualizations requires a few extra libraries:

Python-Igraph
Plotly
Ipywidgets
Additionally, running some of the examples requires:

matplotlib
umap-learn
Installation
Install KeplerMapper with pip:

pip install kmapper
To install from source:

git clone https://github.com/MLWave/kepler-mapper
cd kepler-mapper
pip install -e .
Usage
KeplerMapper adopts the scikit-learn API as much as possible, so it should feel very familiar to anyone who has used these libraries.

Python code

Import the class

import kmapper as km

Some sample data

from sklearn import datasets
data, labels = datasets.make_circles(n_samples=5000, noise=0.03, factor=0.3)

Initialize

mapper = km.KeplerMapper(verbose=1)

Fit to and transform the data

projected_data = mapper.fit_transform(data, projection=[0,1]) # X-Y axis

Create dictionary called ‘graph’ with nodes, edges and meta-information

graph = mapper.map(projected_data, data, n_cubes=10)

Visualize it

mapper.visualize(graph, path_html=“make_circles_keplermapper_output.html”,
title=“make_circles(n_samples=5000, noise=0.03, factor=0.3)”)
Disclaimer
Standard MIT disclaimer applies, see DISCLAIMER.md for full text. Development status is Alpha.

Cite
Nathaniel Saul, & Hendrik Jacob van Veen. (2017, November 17). MLWave/kepler-mapper: 186f (Version 1.0.1). Zenodo. http://doi.org/10.5281/zenodo.1054444