Metadata-Version: 2.1
Name: mantra-dataset
Version: 0.0.0
Summary: A topological datasets for bechmarking higher order methods.
Author-email: Ernst Röell <ernst.roeell@helmholtz-munich.de>, Bastian Rieck <bastian.rieck@helmholtz-munich.de>
Maintainer-email: Ernst Röell <ernst.roeell@helmholtz-munich.de>
Keywords: topology,deep learning,topology,tda
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: torch
Requires-Dist: torch_geometric

# MANTRADataset

# Structure

**mantra** Contains the code for the dataset definition for PyTorch Geometric.

**dataprocessing** Contains the code to publish the dataset to a github release, including the preprocessing scripts. NOTE: We might want to move this to another folder, or rename.

## Dataset

The raw datasets, consisting of the 2 and 3 manifolds with up to 10 vertices, can be downloaded under releases. A pytorch geometric wrapper for the dataset is installable via the following command.

```{python}
pip install "git+https://github.com/aidos-lab/MANTRADataset/#subdirectory=mantra"
```

After installation the dataset can be used with the follwing snippet.

```{python}
from mantra.simplicial import SimplicialDataset

dataset = SimplicialDataset(root="./data", manifold="2")
```

**Warning** Since the repository is private, the dataset can not download the data from the github release due to access restrictions. Hence one has to manually download the "2_manifolds.json.gz" or "3_manifolds.json.gz" to the raw folder for the code to run correctly.
