Metadata-Version: 1.1
Name: jwalk
Version: 0.5.3
Summary: Representational learning on graphs
Home-page: https://github.com/jwplayer/jwalk
Author: Kamil Sindi, Nir Yungster
Author-email: kamil@jwplayer.com, nir@jwplayer.com
License: Apache License 2.0
Description: jwalk
        =====
        
        .. image:: https://travis-ci.org/jwplayer/jwalk.svg?branch=master
            :target: https://travis-ci.org/jwplayer/jwalk
            :alt: Build Status
        
        .. image:: https://readthedocs.org/projects/jwalk/badge/?version=latest
            :target: http://jwalk.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        
        jwalk performs random walks on a graph and learns representations for nodes
        using Word2Vec. It also has options to train existing models online and specify
        weights.
        
        Install
        -------
        
        ::
        
            pip install -U jwalk
        
        Build
        -----
        
        ::
        
            make build
        
        Usage
        -----
        
        ::
        
            jwalk -i tests/data/karate.edgelist -o karate.emb --delimiter=' '
        
        To see the full list of options:
        
        ::
        
            jwalk --help
        
            Prompt parameters:
              debug:            drop a debugger if an exception is raised
              delimiter:        delimiter for input file
              embedding-size:   dimension of word2vec embedding (default=200)
              has-header:       boolean if csv has header row
              help (-h):        argparse help
              input (-i):       file input (edgelist of 2/3 cols or adjacency matrix)
              log-level (-l)    logging level (default=INFO)
              model (-m):       use a pre-existing model
              num-walks (-n):   number of of random walks per graph (default=1)
              output (-o):      file output
              stats:            boolean to calculate walk statistics [requires pandas]
              undirected:       make graph undirected
              walk-length:      length of random walks (default=10)
              window-size:      word2vec window size (default=5)
              workers:          number of workers (default=multiprocessing.cpu_count)
        
        
        Input File
        ~~~~~~~~~~
        
        The input file can be of the following formats:
        
        - Edgelist: CSV with 2 or 3 columns denoting the source, target and (optional)
          weight.
          There are CLI options to specify the delimiter and whether the file has
          a header (default=False).
          The CSV file is loaded using numpy if pandas is not installed. We strongly
          recommend using pandas to load the CSV as it's a lot faster.
        
        - Graph: If the file has an extension that is ".npz", jwalk will assume
          that it is a `SciPy CSR matrix <https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.sparse.csr_matrix.html>`_.
          Included must be keys of data, indices, indptr, shape and labels
          (default=None) where labels are the node labels.
          For an example, see tests/data/karate.npz.
        
        
        Test
        ----
        
        Running unit tests::
        
            make test
        
        Running linter::
        
            make lint
        
        Running tox::
        
            make test-all
        
        Blog
        ----
        Read more about jwalk in our blog post here:
        https://www.jwplayer.com/blog/deepwalk-recommendations/
        
        License
        -------
        
        Apache License 2.0
        
        References
        ----------
        
        - [paper]: arXiv:1403.6652  [cs.SI] "DeepWalk: Online Learning of Social Representations"
        - [paper]: arXiv:1607.00653 [cs.SI] "node2vec: Scalable Feature Learning for Networks"
        
Keywords: deep learning,neural networks,deepwalk
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Environment :: Console
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Cython
Classifier: Topic :: Scientific/Engineering
