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Add docstrings for Wordrank #1378

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merged 6 commits into from
Jun 6, 2017
Merged

Add docstrings for Wordrank #1378

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b0b66d2
added docstring for train method
parulsethi Jun 1, 2017
bffdee6
fix flake8 error
parulsethi Jun 1, 2017
251b6e7
fix flake8 error
parulsethi Jun 1, 2017
fa424e5
fix flake8 error
parulsethi Jun 1, 2017
e28e6bb
fix doc
parulsethi Jun 2, 2017
0d3495c
added info for content generated by wordrank
parulsethi Jun 5, 2017
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2 changes: 1 addition & 1 deletion docs/notebooks/Wordrank_comparisons.ipynb
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Expand Up @@ -173,7 +173,7 @@
" \n",
" # Train using wordrank\n",
" output_file = '{:s}_wr'.format(output_name)\n",
" output_dir = 'wordrank_model' # directory to save embeddings and metadata to\n",
" output_dir = 'model' # directory to save embeddings and metadata to\n",
" if not os.path.isfile(os.path.join(MODELS_DIR, '{:s}.vec'.format(output_file))):\n",
" print('\\nTraining wordrank on {:s} corpus..'.format(corpus_file))\n",
" %time wr_model = Wordrank.train(WR_HOME, corpus_file, output_dir, **wr_params); wr_model\n",
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8 changes: 6 additions & 2 deletions gensim/models/wrappers/wordrank.py
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Expand Up @@ -8,7 +8,7 @@
`Word2Vec` for that.

Example:
>>> model = gensim.models.wrappers.Wordrank('/Users/dummy/wordrank', corpus_file='text8', out_name='wr_model')
>>> model = gensim.models.wrappers.Wordrank.train('/Users/dummy/wordrank', corpus_file='text8', out_name='wr_model')
>>> print model[word] # prints vector for given words

.. [1] https://bitbucket.org/shihaoji/wordrank/
Expand Down Expand Up @@ -47,8 +47,12 @@ class Wordrank(KeyedVectors):
@classmethod
def train(cls, wr_path, corpus_file, out_name, size=100, window=15, symmetric=1, min_count=5, max_vocab_size=0,
sgd_num=100, lrate=0.001, period=10, iter=90, epsilon=0.75, dump_period=10, reg=0, alpha=100,
beta=99, loss='hinge', memory=4.0, cleanup_files=True, sorted_vocab=1, ensemble=0):
beta=99, loss='hinge', memory=4.0, cleanup_files=False, sorted_vocab=1, ensemble=0):
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@menshikh-iv menshikh-iv Jun 3, 2017 •
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What is the reason for change cleanup_file to False?

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cleanup_files=False will not delete the (word/context) embedding files and vocab file generated by wordrank during training, which are saved inside wordrank's directory . Though the train() method loads the final required embedding file before deleting everything that was generated during training but it could be confusing to users who expect to find it after the training is finished.
So, making the default behavior to not delete them could be better to avoid confusion.

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Please enumerate output files (filename and what the file contains) in docstring.

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I've added the output filenames and content info. in out_name param description because it is the directory which contain these files.

"""
The word and context embedding files are generated by wordrank binary and are saved in "out_name" directory
which is created inside wordrank directory. The vocab and cooccurence files are generated using glove code
available inside the wordrank directory. These files are used by the wordrank binary for training.

`wr_path` is the path to the Wordrank directory.
`corpus_file` is the filename of the text file to be used for training the Wordrank model.
Expects file to contain space-separated tokens in a single line
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