Quick Start

Installation

To install the latest version uploaded on PyPI

pip install tda-mapper

Development

  • To install the latest version with dev dependencies

pip install tda-mapper[dev]

  • To install from the latest commit on main branch

pip install git+https://github.com/lucasimi/tda-mapper-python

  • To install from the latest commit of develop branch

pip install git+https://github.com/lucasimi/tda-mapper-python@develop

How To Use

Here’s a minimal example using the circles dataset from scikit-learn to demonstrate how to use tda-mapper:

import numpy as np

from sklearn.datasets import make_circles
from sklearn.decomposition import PCA
from sklearn.cluster import DBSCAN

from tdamapper.core import MapperAlgorithm
from tdamapper.cover import CubicalCover
from tdamapper.plot import MapperPlot

# load a labelled dataset
X, labels = make_circles(n_samples=5000, noise=0.05, factor=0.3, random_state=42)
y = PCA(2, random_state=42).fit_transform(X)

cover = CubicalCover(n_intervals=10, overlap_frac=0.3)
clust = DBSCAN()
graph = MapperAlgorithm(cover, clust).fit_transform(X, y)

# color according to labels
fig = MapperPlot(graph, dim=2, seed=42, iterations=60).plot_plotly(colors=labels)
fig.show(config={'scrollZoom': True})

Original Dataset

Mapper Graph

Original Dataset

Mapper Graph

More examples can be found in the documentation.

Interactive App

Use our Streamlit app to visualize and explore your data without writing code. You can run a live demo directly on Streamlit Cloud, or locally on your machine using the following:

pip install -r app/requirements.txt
streamlit run app/streamlit_app.py