130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright (C) 2017 Anmol Gulati <anmol01gulati@gmail.com>
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# Copyright (C) 2017 Radim Rehurek <radimrehurek@seznam.cz>
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"""Python wrapper around `Varembed model <https://github.com/rguthrie3/MorphologicalPriorsForWordEmbeddings>`_.
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Original paper:`"Morphological Priors for Probabilistic Neural Word Embeddings" <http://arxiv.org/pdf/1608.01056.pdf>`_.
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Notes
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-----
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* This module allows ability to obtain word vectors for out-of-vocabulary words, for the Varembed model.
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* The wrapped model can not be updated with new documents for online training.
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"""
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import logging
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import numpy as np
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from gensim import utils
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from gensim.models.keyedvectors import KeyedVectors
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from gensim.models.word2vec import Vocab
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logger = logging.getLogger(__name__)
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class VarEmbed(KeyedVectors):
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"""Python wrapper using `Varembed <https://github.com/rguthrie3/MorphologicalPriorsForWordEmbeddings>`_.
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Warnings
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--------
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This is **only** python wrapper for `Varembed <https://github.com/rguthrie3/MorphologicalPriorsForWordEmbeddings>`_,
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this allows to load pre-trained models only.
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"""
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def __init__(self):
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self.vector_size = 0
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self.vocab_size = 0
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@classmethod
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def load_varembed_format(cls, vectors, morfessor_model=None):
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"""Load the word vectors into matrix from the varembed output vector files.
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Parameters
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----------
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vectors : dict
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Pickle file containing the word vectors.
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morfessor_model : str, optional
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Path to the trained morfessor model.
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Returns
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-------
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:class:`~gensim.models.wrappers.varembed.VarEmbed`
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Ready to use instance.
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"""
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result = cls()
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if vectors is None:
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raise Exception("Please provide vectors binary to load varembed model")
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d = utils.unpickle(vectors)
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word_to_ix = d['word_to_ix']
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morpho_to_ix = d['morpho_to_ix']
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word_embeddings = d['word_embeddings']
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morpho_embeddings = d['morpheme_embeddings']
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result.load_word_embeddings(word_embeddings, word_to_ix)
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if morfessor_model:
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try:
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import morfessor
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morfessor_model = morfessor.MorfessorIO().read_binary_model_file(morfessor_model)
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result.add_morphemes_to_embeddings(morfessor_model, morpho_embeddings, morpho_to_ix)
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except ImportError:
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# Morfessor Package not found.
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logger.error('Could not import morfessor. Not using morpheme embeddings')
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raise ImportError('Could not import morfessor.')
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logger.info('Loaded varembed model vectors from %s', vectors)
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return result
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def load_word_embeddings(self, word_embeddings, word_to_ix):
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"""Loads the word embeddings.
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Parameters
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----------
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word_embeddings : numpy.ndarray
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Matrix with word-embeddings.
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word_to_ix : dict of (str, int)
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Mapping word to index.
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"""
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logger.info("Loading the vocabulary")
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self.vocab = {}
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self.index2word = []
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counts = {}
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for word in word_to_ix:
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counts[word] = counts.get(word, 0) + 1
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self.vocab_size = len(counts)
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self.vector_size = word_embeddings.shape[1]
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self.syn0 = np.zeros((self.vocab_size, self.vector_size))
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self.index2word = [None] * self.vocab_size
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logger.info("Corpus has %i words", len(self.vocab))
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for word_id, word in enumerate(counts):
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self.vocab[word] = Vocab(index=word_id, count=counts[word])
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self.syn0[word_id] = word_embeddings[word_to_ix[word]]
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self.index2word[word_id] = word
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assert((len(self.vocab), self.vector_size) == self.syn0.shape)
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logger.info("Loaded matrix of %d size and %d dimensions", self.vocab_size, self.vector_size)
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def add_morphemes_to_embeddings(self, morfessor_model, morpho_embeddings, morpho_to_ix):
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"""Include morpheme embeddings into vectors.
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Parameters
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----------
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morfessor_model : :class:`morfessor.baseline.BaselineModel`
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Morfessor model.
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morpho_embeddings : dict
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Pickle file containing morpheme embeddings.
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morpho_to_ix : dict
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Mapping morpheme to index.
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"""
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for word in self.vocab:
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morpheme_embedding = np.array(
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[
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morpho_embeddings[morpho_to_ix.get(m, -1)]
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for m in morfessor_model.viterbi_segment(word)[0]
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]
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).sum(axis=0)
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self.syn0[self.vocab[word].index] += morpheme_embedding
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logger.info("Added morphemes to word vectors")
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