edge2torch v0.1.0

Initial public release of edge2torch.

edge2torch builds sparse PyTorch neural networks from edge lists of named
nodes, with optional feature- and node-level attribution.

Highlights

Compile named edge lists into PyTorch models with compile_graph().
Use one of three supported backends: feedforward, recurrent, or
graphnn.
Align named input data features to compiled model input nodes with
align_features_to_input_nodes().
Customize compiled models with customize_model().
Interpret trained models with Captum-based interpret_model(), including
feature-level attribution and feedforward node-level attribution.
Use optional edge-level metadata such as initial_weight and constraint
to initialize or constrain individual edge weights.
Browse versioned documentation, examples, and API reference.

pip install edge2torch

Optional interpretation support:

pip install "edge2torch[interpret]"