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.
- Python (>= 2.7 or >= 3.3)
Using the plotly visualizations requires a few extra libraries:
Additionally, running some of the examples requires:
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 .
KeplerMapper adopts the scikit-learn API as much as possible, so it should feel very familiar to anyone who has used these libraries.
# 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)")
Standard MIT disclaimer applies, see
DISCLAIMER.md for full text. Development status is Alpha.
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