[論文レビュー] giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

Guillaume Tauzin, Umberto Lupo+7 more|ArODES (HES-SO (https://www.hes-so.ch/))|Apr 6, 2020

Topological and Geometric Data Analysis参考文献 27被引用数 51

ひとこと要約

giotto-tda は、トップロジカルデータ分析（持続的ホモロジーと Mapper）を scikit-learn-compatible パイプラインと統合し、多様なデータ型の TDA ベースの特徴と対話的探索を可能にする Python ライブラリです。

ABSTRACT

We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda.

研究の動機と目的

• Make TDA accessible to the Python data-science community by providing a scikit-learn–compatible API.
• Enable end-to-end pipelines that transform raw data into topological features for ML tasks.
• Support a wide range of input data types through versatile preprocessing and transformers.
• Enhance data exploration and interpretability with interactive plotting and visualization tools.

提案手法

• Provide scikit-learn–compatible components for transforming data into persistence diagrams and other topological representations.
• Offer multiple persistence diagram representations and feature extraction methods (curves, images, kernels) with tunable hyperparameters.
• Integrate with high-performance C++ backends via pybind11 and existing libraries (GUDHI, Ripser, Flagser) for efficient computation.
• Include the Mapper algorithm as a scalable, memory-cacheable pipeline component with interactive visualization.
• Extend pipelines with a TransformerResamplerMixin and enhanced scikit-learn pipelines to accommodate time-delay embeddings and time series forecasting workflows.
• Support deep-learning estimators in pipelines through wrappers to PyTorch/TensorFlow ecosystems (e.g., skorch, scikit-learn interfaces).

実験結果

リサーチクエスチョン

• RQ1 How can topological data analysis be embedded into scalable, end-to-end ML pipelines in Python?
• RQ2 What preprocessing and representations best leverage persistence diagrams and Mapper across univariate/multivariate time series, images, graphs, and simplicial complexes?
• RQ3 Can TDA-based features be effectively tuned and interpreted within a scikit-learn–style workflow?
• RQ4 How does giotto-tda compare to existing Python TDA libraries in terms of feature support and performance?
• RQ5 What is the practicality of integrating Mapper with memory caching and interactive visualization in large-scale workflows?

主な発見

• giotto-tda provides a comprehensive, scikit-learn–compatible interface for persistent homology and Mapper across diverse data types.
• The library leverages high-performance C++ backends (via pybind11) and state-of-the-art libraries to deliver scalable TDA computations.
• It enables end-to-end pipelines that transform raw data into topological features suitable for ML tasks while supporting hyperparameter optimization and feature selection.
• An interactive plotting API and memory caching facilitate real-time hyperparameter tuning of Mapper and topological signatures.
• GIotto-tda demonstrates broad compatibility with time-series embedding techniques and can integrate with deep learning estimators through wrappers.