438 lines
17 KiB
Python
438 lines
17 KiB
Python
|
#!/usr/bin/env python
|
||
|
|
# encoding: utf-8
|
||
|
|
|
||
|
|
"""Produce translation matrix to translate the word from one language to another language, using either
|
||
|
|
standard nearest neighbour method or globally corrected neighbour retrieval method [1]_.
|
||
|
|
|
||
|
|
This method can be used to augment the existing phrase tables with more candidate translations, or
|
||
|
|
filter out errors from the translation tables and known dictionaries [2]_. What's more, It also work
|
||
|
|
for any two sets of named-vectors where there are some paired-guideposts to learn the transformation.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
**How to make translation between two set of word-vectors**
|
||
|
|
|
||
|
|
Initialize a word-vector models
|
||
|
|
|
||
|
|
>>> from gensim.models import KeyedVectors
|
||
|
|
>>> from gensim.test.utils import datapath, temporary_file
|
||
|
|
>>> from gensim.models import TranslationMatrix
|
||
|
|
>>>
|
||
|
|
>>> model_en = KeyedVectors.load_word2vec_format(datapath("EN.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
|
||
|
|
>>> model_it = KeyedVectors.load_word2vec_format(datapath("IT.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
|
||
|
|
|
||
|
|
Define word pairs (that will be used for construction of translation matrix
|
||
|
|
|
||
|
|
>>> word_pairs = [
|
||
|
|
... ("one", "uno"), ("two", "due"), ("three", "tre"), ("four", "quattro"), ("five", "cinque"),
|
||
|
|
... ("seven", "sette"), ("eight", "otto"),
|
||
|
|
... ("dog", "cane"), ("pig", "maiale"), ("fish", "cavallo"), ("birds", "uccelli"),
|
||
|
|
... ("apple", "mela"), ("orange", "arancione"), ("grape", "acino"), ("banana", "banana")
|
||
|
|
... ]
|
||
|
|
|
||
|
|
Fit :class:`~gensim.models.translation_matrix.TranslationMatrix`
|
||
|
|
|
||
|
|
>>> trans_model = TranslationMatrix(model_en, model_it, word_pairs=word_pairs)
|
||
|
|
|
||
|
|
Apply model (translate words "dog" and "one")
|
||
|
|
|
||
|
|
>>> trans_model.translate(["dog", "one"], topn=3)
|
||
|
|
OrderedDict([('dog', [u'cane', u'gatto', u'cavallo']), ('one', [u'uno', u'due', u'tre'])])
|
||
|
|
|
||
|
|
|
||
|
|
Save / load model
|
||
|
|
|
||
|
|
>>> with temporary_file("model_file") as fname:
|
||
|
|
... trans_model.save(fname) # save model to file
|
||
|
|
... loaded_trans_model = TranslationMatrix.load(fname) # load model
|
||
|
|
|
||
|
|
|
||
|
|
**How to make translation between two :class:`~gensim.models.doc2vec.Doc2Vec` models**
|
||
|
|
|
||
|
|
Prepare data and models
|
||
|
|
|
||
|
|
>>> from gensim.test.utils import datapath
|
||
|
|
>>> from gensim.test.test_translation_matrix import read_sentiment_docs
|
||
|
|
>>> from gensim.models import Doc2Vec, BackMappingTranslationMatrix
|
||
|
|
>>>
|
||
|
|
>>> data = read_sentiment_docs(datapath("alldata-id-10.txt"))[:5]
|
||
|
|
>>> src_model = Doc2Vec.load(datapath("small_tag_doc_5_iter50"))
|
||
|
|
>>> dst_model = Doc2Vec.load(datapath("large_tag_doc_10_iter50"))
|
||
|
|
|
||
|
|
Train backward translation
|
||
|
|
|
||
|
|
>>> model_trans = BackMappingTranslationMatrix(data, src_model, dst_model)
|
||
|
|
>>> trans_matrix = model_trans.train(data)
|
||
|
|
|
||
|
|
|
||
|
|
Apply model
|
||
|
|
|
||
|
|
>>> result = model_trans.infer_vector(dst_model.docvecs[data[3].tags])
|
||
|
|
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] Dinu, Georgiana, Angeliki Lazaridou, and Marco Baroni. "Improving zero-shot learning by mitigating the
|
||
|
|
hubness problem", https://arxiv.org/abs/1412.6568
|
||
|
|
.. [2] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean.
|
||
|
|
"Distributed Representations of Words and Phrases and their Compositionality", https://arxiv.org/abs/1310.4546
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
import warnings
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from collections import OrderedDict
|
||
|
|
from gensim import utils
|
||
|
|
from six import string_types
|
||
|
|
|
||
|
|
|
||
|
|
class Space(object):
|
||
|
|
"""An auxiliary class for storing the the words space."""
|
||
|
|
|
||
|
|
def __init__(self, matrix, index2word):
|
||
|
|
"""
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
matrix : iterable of numpy.ndarray
|
||
|
|
Matrix that contains word-vectors.
|
||
|
|
index2word : list of str
|
||
|
|
Words which correspond to the `matrix`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.mat = matrix
|
||
|
|
self.index2word = index2word
|
||
|
|
|
||
|
|
# build a dict to map word to index
|
||
|
|
self.word2index = {}
|
||
|
|
for idx, word in enumerate(self.index2word):
|
||
|
|
self.word2index[word] = idx
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def build(cls, lang_vec, lexicon=None):
|
||
|
|
"""Construct a space class for the lexicon, if it's provided.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`
|
||
|
|
Model from which the vectors will be extracted.
|
||
|
|
lexicon : list of str, optional
|
||
|
|
Words which contains in the `lang_vec`, if `lexicon = None`, the lexicon is all the lang_vec's word.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
:class:`~gensim.models.translation_matrix.Space`
|
||
|
|
Object that stored word-vectors
|
||
|
|
|
||
|
|
"""
|
||
|
|
# `words` to store all the word that
|
||
|
|
# `mat` to store all the word vector for the word in 'words' list
|
||
|
|
words = []
|
||
|
|
mat = []
|
||
|
|
if lexicon is not None:
|
||
|
|
# if the lexicon is not provided, using the all the Keyedvectors's words as default
|
||
|
|
for item in lexicon:
|
||
|
|
words.append(item)
|
||
|
|
mat.append(lang_vec.syn0[lang_vec.vocab[item].index])
|
||
|
|
|
||
|
|
else:
|
||
|
|
for item in lang_vec.vocab.keys():
|
||
|
|
words.append(item)
|
||
|
|
mat.append(lang_vec.syn0[lang_vec.vocab[item].index])
|
||
|
|
|
||
|
|
return Space(mat, words)
|
||
|
|
|
||
|
|
def normalize(self):
|
||
|
|
"""Normalize the word vector's matrix."""
|
||
|
|
self.mat = self.mat / np.sqrt(np.sum(np.multiply(self.mat, self.mat), axis=1, keepdims=True))
|
||
|
|
|
||
|
|
|
||
|
|
class TranslationMatrix(utils.SaveLoad):
|
||
|
|
"""Objects of this class realize the translation matrix which map the source language to the target language.
|
||
|
|
The main methods are:
|
||
|
|
|
||
|
|
We map it to the other language space by computing z = Wx, then return the
|
||
|
|
word whose representation is close to z.
|
||
|
|
|
||
|
|
The details use seen the notebook [3]_
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from gensim.models import KeyedVectors
|
||
|
|
>>> from gensim.test.utils import datapath, temporary_file
|
||
|
|
>>>
|
||
|
|
>>> model_en = KeyedVectors.load_word2vec_format(datapath("EN.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
|
||
|
|
>>> model_it = KeyedVectors.load_word2vec_format(datapath("IT.1-10.cbow1_wind5_hs0_neg10_size300_smpl1e-05.txt"))
|
||
|
|
>>>
|
||
|
|
>>> word_pairs = [
|
||
|
|
... ("one", "uno"), ("two", "due"), ("three", "tre"), ("four", "quattro"), ("five", "cinque"),
|
||
|
|
... ("seven", "sette"), ("eight", "otto"),
|
||
|
|
... ("dog", "cane"), ("pig", "maiale"), ("fish", "cavallo"), ("birds", "uccelli"),
|
||
|
|
... ("apple", "mela"), ("orange", "arancione"), ("grape", "acino"), ("banana", "banana")
|
||
|
|
... ]
|
||
|
|
>>>
|
||
|
|
>>> trans_model = TranslationMatrix(model_en, model_it)
|
||
|
|
>>> trans_model.train(word_pairs)
|
||
|
|
>>> trans_model.translate(["dog", "one"], topn=3)
|
||
|
|
OrderedDict([('dog', [u'cane', u'gatto', u'cavallo']), ('one', [u'uno', u'due', u'tre'])])
|
||
|
|
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [3] https://github.com/RaRe-Technologies/gensim/blob/3.2.0/docs/notebooks/translation_matrix.ipynb
|
||
|
|
|
||
|
|
"""
|
||
|
|
def __init__(self, source_lang_vec, target_lang_vec, word_pairs=None, random_state=None):
|
||
|
|
"""
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
source_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`
|
||
|
|
Word vectors for source language.
|
||
|
|
target_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`
|
||
|
|
Word vectors for target language.
|
||
|
|
word_pairs : list of (str, str), optional
|
||
|
|
Pairs of words that will be used for training.
|
||
|
|
random_state : {None, int, array_like}, optional
|
||
|
|
Seed for random state.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
self.source_word = None
|
||
|
|
self.target_word = None
|
||
|
|
self.source_lang_vec = source_lang_vec
|
||
|
|
self.target_lang_vec = target_lang_vec
|
||
|
|
|
||
|
|
self.random_state = utils.get_random_state(random_state)
|
||
|
|
self.translation_matrix = None
|
||
|
|
self.source_space = None
|
||
|
|
self.target_space = None
|
||
|
|
|
||
|
|
if word_pairs is not None:
|
||
|
|
if len(word_pairs[0]) != 2:
|
||
|
|
raise ValueError("Each training data item must contain two different language words.")
|
||
|
|
self.train(word_pairs)
|
||
|
|
|
||
|
|
def train(self, word_pairs):
|
||
|
|
"""Build the translation matrix that mapping from source space to target space.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
word_pairs : list of (str, str), optional
|
||
|
|
Pairs of words that will be used for training.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.source_word, self.target_word = zip(*word_pairs)
|
||
|
|
|
||
|
|
self.source_space = Space.build(self.source_lang_vec, set(self.source_word))
|
||
|
|
self.target_space = Space.build(self.target_lang_vec, set(self.target_word))
|
||
|
|
|
||
|
|
self.source_space.normalize()
|
||
|
|
self.target_space.normalize()
|
||
|
|
|
||
|
|
m1 = self.source_space.mat[[self.source_space.word2index[item] for item in self.source_word], :]
|
||
|
|
m2 = self.target_space.mat[[self.target_space.word2index[item] for item in self.target_word], :]
|
||
|
|
|
||
|
|
self.translation_matrix = np.linalg.lstsq(m1, m2, -1)[0]
|
||
|
|
|
||
|
|
def save(self, *args, **kwargs):
|
||
|
|
"""Save the model to file but ignoring the `source_space` and `target_space`"""
|
||
|
|
kwargs['ignore'] = kwargs.get('ignore', ['source_space', 'target_space'])
|
||
|
|
super(TranslationMatrix, self).save(*args, **kwargs)
|
||
|
|
|
||
|
|
def apply_transmat(self, words_space):
|
||
|
|
"""Map the source word vector to the target word vector using translation matrix.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
words_space : :class:`~gensim.models.translation_matrix.Space`
|
||
|
|
Object that constructed for those words to be translate.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
:class:`~gensim.models.translation_matrix.Space`
|
||
|
|
Object that constructed for those mapped words.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return Space(np.dot(words_space.mat, self.translation_matrix), words_space.index2word)
|
||
|
|
|
||
|
|
def translate(self, source_words, topn=5, gc=0, sample_num=None, source_lang_vec=None, target_lang_vec=None):
|
||
|
|
"""Translate the word from the source language to the target language.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
source_words : {str, list of str}
|
||
|
|
Single word or a list of words to be translated
|
||
|
|
topn : int, optional
|
||
|
|
Number of words than will be returned as translation for each `source_words`
|
||
|
|
gc : int, optional
|
||
|
|
Define translation algorithm, if `gc == 0` - use standard NN retrieval,
|
||
|
|
otherwise, use globally corrected neighbour retrieval method (as described in [1]_).
|
||
|
|
sample_num : int, optional
|
||
|
|
Number of word to sample from the source lexicon, if `gc == 1`, then `sample_num` **must** be provided.
|
||
|
|
source_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`, optional
|
||
|
|
New source language vectors for translation, by default, used the model's source language vector.
|
||
|
|
target_lang_vec : :class:`~gensim.models.keyedvectors.KeyedVectors`, optional
|
||
|
|
New target language vectors for translation, by default, used the model's target language vector.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
:class:`collections.OrderedDict`
|
||
|
|
Ordered dict where each item is `word`: [`translated_word_1`, `translated_word_2`, ...]
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
if isinstance(source_words, string_types):
|
||
|
|
# pass only one word to translate
|
||
|
|
source_words = [source_words]
|
||
|
|
|
||
|
|
# If the language word vector not provided by user, use the model's
|
||
|
|
# language word vector as default
|
||
|
|
if source_lang_vec is None:
|
||
|
|
warnings.warn(
|
||
|
|
"The parameter source_lang_vec isn't specified, "
|
||
|
|
"use the model's source language word vector as default."
|
||
|
|
)
|
||
|
|
source_lang_vec = self.source_lang_vec
|
||
|
|
|
||
|
|
if target_lang_vec is None:
|
||
|
|
warnings.warn(
|
||
|
|
"The parameter target_lang_vec isn't specified, "
|
||
|
|
"use the model's target language word vector as default."
|
||
|
|
)
|
||
|
|
target_lang_vec = self.target_lang_vec
|
||
|
|
|
||
|
|
# If additional is provided, bootstrapping vocabulary from the source language word vector model.
|
||
|
|
if gc:
|
||
|
|
if sample_num is None:
|
||
|
|
raise RuntimeError(
|
||
|
|
"When using the globally corrected neighbour retrieval method, "
|
||
|
|
"the `sample_num` parameter(i.e. the number of words sampled from source space) must be provided."
|
||
|
|
)
|
||
|
|
lexicon = set(source_lang_vec.index2word)
|
||
|
|
addition = min(sample_num, len(lexicon) - len(source_words))
|
||
|
|
lexicon = self.random_state.choice(list(lexicon.difference(source_words)), addition)
|
||
|
|
source_space = Space.build(source_lang_vec, set(source_words).union(set(lexicon)))
|
||
|
|
else:
|
||
|
|
source_space = Space.build(source_lang_vec, source_words)
|
||
|
|
target_space = Space.build(target_lang_vec, )
|
||
|
|
|
||
|
|
# Normalize the source vector and target vector
|
||
|
|
source_space.normalize()
|
||
|
|
target_space.normalize()
|
||
|
|
|
||
|
|
# Map the source language to the target language
|
||
|
|
mapped_source_space = self.apply_transmat(source_space)
|
||
|
|
|
||
|
|
# Use the cosine similarity metric
|
||
|
|
sim_matrix = -np.dot(target_space.mat, mapped_source_space.mat.T)
|
||
|
|
|
||
|
|
# If `gc=1`, using corrected retrieval method
|
||
|
|
if gc:
|
||
|
|
srtd_idx = np.argsort(np.argsort(sim_matrix, axis=1), axis=1)
|
||
|
|
sim_matrix_idx = np.argsort(srtd_idx + sim_matrix, axis=0)
|
||
|
|
else:
|
||
|
|
sim_matrix_idx = np.argsort(sim_matrix, axis=0)
|
||
|
|
|
||
|
|
# Translate the words and for each word return the `topn` similar words
|
||
|
|
translated_word = OrderedDict()
|
||
|
|
for idx, word in enumerate(source_words):
|
||
|
|
translated_target_word = []
|
||
|
|
# Search the most `topn` similar words
|
||
|
|
for j in range(topn):
|
||
|
|
map_space_id = sim_matrix_idx[j, source_space.word2index[word]]
|
||
|
|
translated_target_word.append(target_space.index2word[map_space_id])
|
||
|
|
translated_word[word] = translated_target_word
|
||
|
|
return translated_word
|
||
|
|
|
||
|
|
|
||
|
|
class BackMappingTranslationMatrix(utils.SaveLoad):
|
||
|
|
"""Realize the BackMapping translation matrix which map the source model's document vector
|
||
|
|
to the target model's document vector(old model).
|
||
|
|
|
||
|
|
BackMapping translation matrix is used to learn a mapping for two document vector space which we
|
||
|
|
specify as source document vector and target document vector. The target document vector are trained
|
||
|
|
on superset corpus of source document vector, we can incrementally increase the vector in
|
||
|
|
the old model through the BackMapping translation matrix.
|
||
|
|
|
||
|
|
the details use seen the notebook [3]_.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from gensim.test.utils import datapath
|
||
|
|
>>> from gensim.test.test_translation_matrix import read_sentiment_docs
|
||
|
|
>>> from gensim.models import Doc2Vec, BackMappingTranslationMatrix
|
||
|
|
>>>
|
||
|
|
>>> data = read_sentiment_docs(datapath("alldata-id-10.txt"))[:5]
|
||
|
|
>>> src_model = Doc2Vec.load(datapath("small_tag_doc_5_iter50"))
|
||
|
|
>>> dst_model = Doc2Vec.load(datapath("large_tag_doc_10_iter50"))
|
||
|
|
>>>
|
||
|
|
>>> model_trans = BackMappingTranslationMatrix(src_model, dst_model)
|
||
|
|
>>> trans_matrix = model_trans.train(data)
|
||
|
|
>>>
|
||
|
|
>>> result = model_trans.infer_vector(dst_model.docvecs[data[3].tags])
|
||
|
|
|
||
|
|
"""
|
||
|
|
def __init__(self, source_lang_vec, target_lang_vec, tagged_docs=None, random_state=None):
|
||
|
|
"""
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
source_lang_vec : :class:`~gensim.models.doc2vec.Doc2Vec`
|
||
|
|
Source Doc2Vec model.
|
||
|
|
target_lang_vec : :class:`~gensim.models.doc2vec.Doc2Vec`
|
||
|
|
Target Doc2Vec model.
|
||
|
|
tagged_docs : list of :class:`~gensim.models.doc2vec.TaggedDocument`, optional.
|
||
|
|
Documents that will be used for training, both the source language document vector and
|
||
|
|
target language document vector trained on those tagged documents.
|
||
|
|
random_state : {None, int, array_like}, optional
|
||
|
|
Seed for random state.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.tagged_docs = tagged_docs
|
||
|
|
self.source_lang_vec = source_lang_vec
|
||
|
|
self.target_lang_vec = target_lang_vec
|
||
|
|
|
||
|
|
self.random_state = utils.get_random_state(random_state)
|
||
|
|
self.translation_matrix = None
|
||
|
|
|
||
|
|
if tagged_docs is not None:
|
||
|
|
self.train(tagged_docs)
|
||
|
|
|
||
|
|
def train(self, tagged_docs):
|
||
|
|
"""Build the translation matrix that mapping from the source model's vector to target model's vector
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
tagged_docs : list of :class:`~gensim.models.doc2vec.TaggedDocument`, Documents
|
||
|
|
that will be used for training, both the source language document vector and
|
||
|
|
target language document vector trained on those tagged documents.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
Translation matrix that mapping from the source model's vector to target model's vector.
|
||
|
|
|
||
|
|
"""
|
||
|
|
m1 = [self.source_lang_vec.docvecs[item.tags].flatten() for item in tagged_docs]
|
||
|
|
m2 = [self.target_lang_vec.docvecs[item.tags].flatten() for item in tagged_docs]
|
||
|
|
|
||
|
|
self.translation_matrix = np.linalg.lstsq(m2, m1, -1)[0]
|
||
|
|
return self.translation_matrix
|
||
|
|
|
||
|
|
def infer_vector(self, target_doc_vec):
|
||
|
|
"""Translate the target model's document vector to the source model's document vector
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
target_doc_vec : numpy.ndarray
|
||
|
|
Document vector from the target document, whose document are not in the source model.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
Vector `target_doc_vec` in the source model.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return np.dot(target_doc_vec, self.translation_matrix)
|