import networkx as nx
import gravlearn as gn
import torch
# Load data
G = nx.karate_club_graph()
A = nx.adjacency_matrix(G)
labels = [G.nodes[i]["club"] for i in G.nodes]
# Generate the sequence for demo
sampler = gn.RandomWalkSampler(A, walk_length=40, p=1, q=1)
walks = [sampler.sampling(i) for _ in range(10) for i in range(A.shape[0])]
# Training
model = gravlearn.Word2Vec(A.shape[0], 32) # Embedding based on set
dist_metric = gravlearn.DistanceMetrics.EUCLIDEAN
model = gravlearn.train(model, walks, device = device, bags =A ,window_length=5, dist_metric=dist_metric)
# Embedding
emb = model.forward(torch.arange(A.shape[0]))
1 maintainer
Metadata
1 maintainer
Release files for gravlearn 0.0.10
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| gravlearn-0.0.10-py3-none-any.whl | Python 3 | none | any | Details for gravlearn-0.0.10-py3-none-any.whl |
Release files / gravlearn-0.0.10-py3-none-any.whl
| Download URL | gravlearn-0.0.10-py3-none-any.whl |
|---|---|
| Size | 13.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0a3d0fcdf27ce8534b07a506e99fb18fed8594beb1502b3b1be38cfb4af4dc9a
|
|
BLAKE2b-256 checksum How to use checksums |
ff722f6c6647c7547eff841f2cca6bc827b01db0ec7393d3b8523ee2baf9ad0b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.13
|
Security for gravlearn 0.0.10
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
