tda-mapper 0.5.1

A simple and efficient Python implementation of Mapper algorithm for Topological Data Analysis

tda-mapper

A simple and efficient Python implementation of Mapper algorithm for Topological Data Analysis

• Installation: pip install tda-mapper

• Documentation: https://tda-mapper.readthedocs.io/en/main/

The Mapper algorithm is a well-known technique in the field of topological data analysis that allows data to be represented as a graph. Mapper is used in various fields such as machine learning, data mining, and social sciences, due to its ability to preserve topological features of the underlying space, providing a visual representation that facilitates exploration and interpretation. For an in-depth coverage of Mapper you can read the original paper.

Step 1	Step 2	Step 3	Step 4
Chose lens	Cover image	Run clustering	Build graph

Example

Here you can find an example to use to kickstart your analysis. In this toy-example we use a two-dimensional dataset of two concentric circles. The Mapper graph is a topological summary of the whole point cloud.

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 MapperLayoutInteractive

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

mapper_algo = MapperAlgorithm(
    cover=CubicalCover(
        n_intervals=10,
        overlap_frac=0.3),
    clustering=DBSCAN())
mapper_graph = mapper_algo.fit_transform(X, lens)

mapper_plot = MapperLayoutInteractive(
    mapper_graph,
    colors=y,                       # color according to categorical values
    cmap='jet',                     # Jet colormap, for classes
    agg=np.nanmean,                 # aggregate on nodes according to mean
    dim=2,
    iterations=60,
    seed=42,
    width=600,
    height=600)

fig_mean = mapper_plot.plot()
fig_mean.show(config={'scrollZoom': True})

mapper_plot.update(                 # reuse the plot with the same positions
    colors=y,
    cmap='viridis',                 # viridis colormap, for ranges
    agg=np.nanstd,                  # aggregate on nodes according to std
)

fig_std = mapper_plot.plot()
fig_std.show(config={'scrollZoom': True})

Dataset	Mapper graph (average)	Mapper graph (deviation)

More examples can be found in the documentation https://tda-mapper.readthedocs.io/en/main/.

Citations

To cite tda-mapper in your work you can use the Zenodo archive https://doi.org/10.5281/zenodo.10642381.

In the archive you can find a permanent reference to the exact version you used in your work.

For example, to cite version v0.4.0 you can use:

Simi, L. (2024). tda-mapper (v0.4.0). Zenodo. https://doi.org/10.5281/zenodo.10655755

BibTeX entry:

@software{tda-mapper_v0.4.0,
  author       = {Simi, Luca},
  title        = {tda-mapper},
  month        = feb,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v0.4.0},
  doi          = {10.5281/zenodo.10655755},
  url          = {https://doi.org/10.5281/zenodo.10655755}
}