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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. This example demonstrates how to apply the Mapper algorithm on a synthetic dataset (concentric circles). The goal is to extract a topological graph representation using PCA as a lens and DBSCAN for clustering. We proceed as follows:

import matplotlib.pyplot as plt
from sklearn.datasets import make_circles

import numpy as np
from sklearn.decomposition import PCA
from sklearn.cluster import DBSCAN

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

# Generate toy dataset
X, labels = make_circles(n_samples=5000, noise=0.05, factor=0.3, random_state=42)
plt.figure(figsize=(5, 5))
plt.scatter(X[:,0], X[:,1], c=labels, s=0.25, cmap="jet")
plt.axis("off")
plt.show()

# Apply PCA as lens
y = PCA(2, random_state=42).fit_transform(X)

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

# Visualize the Mapper graph
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

Left: the original dataset consisting of two concentric circles with noise, colored by class label. Right: the resulting Mapper graph, built from the PCA projection and clustered using DBSCAN. The two concentric circles are well identified by the connected components in the 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:

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. The first time you run the app locally, you may need to install the required dependencies from the requirements.txt file by running

pip install -r app/requirements.txt

then run the app locally with

streamlit run app/streamlit_app.py

Interactive App