[WIP] Add sent2vec in Gensim

gensim/models/wrappers/sent2vec.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Author: Sourav Singh <ssouravsingh12@gmail.com>
+# Copyright (C) 2017 Radim Rehurek <me@radimrehurek.com>
+# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
+
+
+"""
+Python wrapper around sentence representation learning from sent2vec, a library for unsupervised 
+learning of short texts or sentences[1].
+This module allows training a word embedding from a training corpus with the additional ability
+to obtain word vectors for out-of-vocabulary words, using the fastText C implementation.
+The wrapped model can NOT be updated with new documents for online training -- use gensim's
+`Word2Vec` for that.
+
+Example:
+>>> from gensim.models.wrappers import FastText
+>>> model = fasttext.FastText.train('/Users/kofola/fastText/fasttext', corpus_file='text8')
+>>> print model['forests']  # prints vector for given out-of-vocabulary word
+
+.. [1] https://github.com/epfml/sent2vec
+
+"""
+
+
+import logging
+
+import numpy as np
+from numpy import zeros, ones, vstack, sum as np_sum, empty, float32 as REAL
+
+from gensim.models.word2vec import Word2Vec, train_sg_pair, train_cbow_pair
+from gensim.models.wrappers.fasttext import FastTextKeyedVectors
+from gensim.models.wrappers.fasttext import FastText as Ft_Wrapper, compute_ngrams, ft_hash
+
+logger = logging.getLogger(__name__)
+
+
+class Sent2Vec(Word2Vec):
+    """
+    Class for word vector training using Sent2Vec. Communication between Sent2Vec and Python
+    takes place by working with data files on disk and calling the Sent2Vec binary with
+    subprocess.call().
+    """
+
+    def initialize_word_vectors(self):
+        self.wv = Sent2VecKeyedVectors()
+
+    @classmethod
+    def train(cls, ft_path, corpus_file, output_file=None, model='cbow', size=100, alpha=0.025, window=5, min_count=5,
+            word_ngrams=1, loss='ns', sample=1e-3, negative=5, iter=5, min_n=3, max_n=6, sorted_vocab=1, threads=12):
+        """
+        `ft_path` is the path to the Sent2Vec executable, e.g. `/home/kofola/sent2vec/sent2vec`.
+        `corpus_file` is the filename of the text file to be used for training the Sent2Vec model.
+        Expects file to contain utf-8 encoded text.
+        `model` defines the training algorithm. By default, cbow is used. Accepted values are
+        'cbow', 'skipgram'.
+        `size` is the dimensionality of the feature vectors.
+        `window` is the maximum distance between the current and predicted word within a sentence.
+        `alpha` is the initial learning rate.
+        `min_count` = ignore all words with total occurrences lower than this.
+        `word_ngram` = max length of word ngram
+        `loss` = defines training objective. Allowed values are `hs` (hierarchical softmax),
+        `ns` (negative sampling) and `softmax`. Defaults to `ns`
+        `sample` = threshold for configuring which higher-frequency words are randomly downsampled;
+            default is 1e-3, useful range is (0, 1e-5).
+        `negative` = the value for negative specifies how many "noise words" should be drawn
+        (usually between 5-20). Default is 5. If set to 0, no negative samping is used.
+        Only relevant when `loss` is set to `ns`
+        `iter` = number of iterations (epochs) over the corpus. Default is 5.
+        `min_n` = min length of char ngrams to be used for training word representations. Default is 3.
+        `max_n` = max length of char ngrams to be used for training word representations. Set `max_n` to be
+        lesser than `min_n` to avoid char ngrams being used. Default is 6.
+        `sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before
+        assigning word indexes.
+        `threads` = number of threads to use. Default is 12.
+        """
+        ft_path = ft_path
+        output_file = output_file or os.path.join(tempfile.gettempdir(), 'ft_model')
+        ft_args = {
+            'input': corpus_file,
+            'output': output_file,
+            'lr': alpha,
+            'dim': size,
+            'ws': window,
+            'epoch': iter,
+            'minCount': min_count,
+            'wordNgrams': word_ngrams,
+            'neg': negative,
+            'loss': loss,
+            'minn': min_n,
+            'maxn': max_n,
+            'thread': threads,
+            't': sample
+        }
+        cmd = [ft_path, model]
+        for option, value in ft_args.items():
+            cmd.append("-%s" % option)
+            cmd.append(str(value))
+
+        output = utils.check_output(args=cmd)
+        model = cls.load_fasttext_format(output_file)
+        cls.delete_training_files(output_file)
+        return model
+
+    def save(self, *args, **kwargs):
+        # don't bother storing the cached normalized vectors
+        kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_all_norm'])
+        super(Sent2Vec, self).save(*args, **kwargs)