TopoGeoML 0.0.8 is beta scientific software. This version includes post-0.0.7 correctness fixes; public APIs can change before 1.0.
TopoGeoML is a Python library for using topology in machine learning. It turns point clouds into fixed-length features for scikit-learn, constructs simplicial and Hodge operators, and provides experimental topology losses for PyTorch. The repository also contains a separate graph-classification research record. These are different deliverables: a working library does not imply a positive research result.
A persistence engine returns birth–death intervals, not a fitted feature transformer, a training loss, or an auditable experiment. Connecting these pieces by hand risks mixing training and evaluation data or assigning gradients to the wrong critical edges. TopoGeoML supplies those connections; it does not replace the underlying numerical libraries.
The repository-specific work is the assembly of train-fitted feature calibration, typed diagram provenance, optional PyTorch critical-value routing, simplicial operators, and evidence-oriented experiment runners. Vietoris–Rips persistent homology comes from ripser; the optional cubical and tied-generator computations use GUDHI (Geometry Understanding in Higher Dimensions); classical numerical and model interfaces come from NumPy, SciPy, scikit-learn, NetworkX, and PyTorch. Persistence images, Betti curves, and Hodge Laplacians are established methods. The code and test record do not establish a new topology theorem or superiority over competing toolkits. To cite this release, use CITATION.cff with version = {0.0.8}.
Use Python 3.11 or 3.12, Git, an internet connection for installation, and a Portable Operating System Interface (POSIX) shell (Linux/macOS). Run these commands exactly, starting outside the repository:
git clone https://github.com/smaniches/TopoGeoML.git
cd TopoGeoML
python3 -m venv .venv
. .venv/bin/activate
python -m pip install .
python examples/circles_vs_lines.pyA successful run prints Building synthetic dataset, cross-validation and training scores, and a Fit provenance block. Those scores are observations from your run, not guaranteed benchmark targets. This installs the current checkout, which can differ from the most recently published package version. Windows commands and the smaller direct application programming interface (API) example are in Usage.
Vietoris–Rips complexes can become expensive for large point clouds; topology summaries discard information; and feature calibration must be learned only on training data. The differentiable one-dimensional homology path rejects ambiguous critical-edge gradients by default rather than inventing a derivative. Explicit GUDHI tie selection does not prove a unique derivative. The graph study includes invalidated, negative, and unfinished experiments. See Limitations before relying on scientific or scaling claims.
Architecture · Design decisions · Limitations · Usage and troubleshooting
Research evidence and reproduction: Status · Claims to evidence · Reproducing results.