tda-mapper
tda-mapper is a Python library built around the Mapper algorithm, a core technique in Topological Data Analysis (TDA) for extracting topological structure from complex data. Designed for computational efficiency and scalability, it leverages optimized spatial search methods to support high-dimensional datasets. The library is well-suited for integration into machine learning pipelines, unsupervised learning tasks, and exploratory data analysis.
Further details in the documentation and in the paper.
Core features
Efficient construction
Leverages optimized spatial search techniques and parallelization to accelerate the construction of Mapper graphs, supporting the analysis of high-dimensional datasets.
Scikit-learn integration
Provides custom estimators that are fully compatible with scikit-learn’s API, enabling seamless integration into scikit-learn pipelines for tasks such as dimensionality reduction, clustering, and feature extraction.
Flexible visualization
Multiple visualization backends supported (Plotly, Matplotlib, PyVis) for generating high-quality Mapper graph representations with adjustable layouts and styling.
Interactive app
Provides an interactive web-based interface for dynamic exploration of Mapper graph structures, offering real-time adjustments to parameters and visualizations.
Background
The Mapper algorithm extracts topological features from complex datasets, representing them as graphs that highlight clusters, transitions, and key structural patterns. These insights reveal hidden data relationships and are applicable across diverse fields, including social sciences, biology, and machine learning. For an in-depth overview of Mapper, including its mathematical foundations and practical applications, read the original paper.
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Choose lens |
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Run clustering |
Build graph |
User's Guide
API Reference