Getting started with Mapper¶
In this notebook we explore a few of the core features included in
giotto-tda’s implementation of the Mapper
If you are looking at a static version of this notebook and would like to run its contents, head over to GitHub and download the source.
An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists
An Introduction to Topological Data Analysis for Physicists: From LGM to FRBs
# Data wrangling import numpy as np import pandas as pd # Not a requirement of giotto-tda, but is compatible with the gtda.mapper module # Data viz from gtda.plotting import plot_point_cloud # TDA magic from gtda.mapper import ( CubicalCover, make_mapper_pipeline, Projection, plot_static_mapper_graph, plot_interactive_mapper_graph, MapperInteractivePlotter ) # ML tools from sklearn import datasets from sklearn.cluster import DBSCAN from sklearn.decomposition import PCA
Generate and visualise data¶
As a simple example, let’s generate a two-dimensional point cloud of two concentric circles. The goal will be to examine how Mapper can be used to generate a topological graph that captures the salient features of the data.
data, _ = datasets.make_circles(n_samples=5000, noise=0.05, factor=0.3, random_state=42) plot_point_cloud(data)