Metadata-Version: 2.1
Name: fastnode2vec
Version: 0.0.3
Summary: Fast implementation of node2vec
Home-page: https://github.com/louisabraham/fastnode2vec
Author: Louis Abraham
Author-email: louis.abraham@yahoo.fr
License: MIT
Description: [![PyPI
        version](https://badge.fury.io/py/fastnode2vec.svg)](https://badge.fury.io/py/fastnode2vec)
        
        # fastnode2vec
        
        *Really* fast implementation of node2vec based on [numba](https://numba.pydata.org/) and [gensim](https://radimrehurek.com/gensim/).
        
        ## API
        
        `Node2Vec` inherits from gensim's [`Word2Vec`](https://radimrehurek.com/gensim/models/word2vec.html), all its APi is valid.
        
        ```python
        from fastnode2vec import Graph, Node2Vec
        
        graph = Graph([("a", "b"), ("b", "c"), ("c", "a"), ("a", "d")],
                      directed=False, weighted=False)
        
        # or
        graph = Graph([("a", "b", 1), ("b", "c", 2), ("c", "a", 3), ("a", "d", 4)],
                      directed=False, weighted=True)
        
        n2v = Node2Vec(graph, dim=10, walk_length=100, context=10, p=2.0, q=0.5, workers=2)
        
        n2v.train(epochs=100)
        
        print(n2v.wv["a"])
        ```
        
        ## CLI
        
        
        ```
        Usage: fastnode2vec [OPTIONS] FILENAME
        
        Options:
          --directed
          --dim INTEGER          [required]
          --p FLOAT
          --q FLOAT
          --walk-length INTEGER  [required]
          --context INTEGER
          --epochs INTEGER       [required]
          --workers INTEGER
          --batch-walks INTEGER
          --debug PATH
          --output PATH
          --help                 Show this message and exit.
        
        ```
        
        
        Compute embeddings of the [Gnutella peer-to-peer network](https://snap.stanford.edu/data/p2p-Gnutella08.html):
        
        ```
        wget https://snap.stanford.edu/data/p2p-Gnutella08.txt.gz
        fastnode2vec p2p-Gnutella08.txt.gz --dim 16 --walk-length 100 --epochs 10 --workers 2
        ```
        
        ## Load embeddings produced by the CLI
        
        Just use the [`Word2Vec`](https://radimrehurek.com/gensim/models/word2vec.html) API.
        
        ```python
        from gensim.models import KeyedVectors
        
        wv = KeyedVectors.load("p2p-Gnutella08.txt.gz.wv", mmap='r')
        ```
        
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
