To install the latest version uploaded on PyPI
pip install tda-mapper- 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@developHere's a minimal example using the circles dataset from
scikit-learn to demonstrate how to use tda-mapper:
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 |
|---|---|
 |
 |
More examples can be found in the documentation.
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