A simple and efficient Python implementation of Mapper algorithm for Topological Data Analysis
Project description
tda-mapper
This Python package provides a simple and efficient implementation of Mapper algorithm.
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Installation:
pip install tda-mapper
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Documentation: https://tda-mapper.readthedocs.io/en/main/
About Mapper
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 |
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Chose a lens | Cover the image of the lens | Perform clustering on the pullback cover | Build the Mapper graph |
Usage
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 MapperPlot
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 = MapperPlot(
X, 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)
fig_mean = mapper_plot.plot(
width=600,
height=600)
fig_mean.show(config={'scrollZoom': True})
fig_std = mapper_plot.with_colors( # reuse the plot with the same positions
colors=y,
cmap='viridis', # viridis colormap, for ranges
agg=np.nanstd, # aggregate on nodes according to std
).plot(
width=600,
height=600)
fig_std.show(config={'scrollZoom': True})
Dataset | Mapper graph (average) | Mapper graph (deviation) |
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