2038 lines
83 KiB
Python
2038 lines
83 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Author: Shiva Manne <manneshiva@gmail.com>
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# Copyright (C) 2018 RaRe Technologies s.r.o.
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# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
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"""This module implements word vectors and their similarity look-ups.
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Since trained word vectors are independent from the way they were trained (:class:`~gensim.models.word2vec.Word2Vec`,
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:class:`~gensim.models.fasttext.FastText`, :class:`~gensim.models.wrappers.wordrank.WordRank`,
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:class:`~gensim.models.wrappers.varembed.VarEmbed` etc), they can be represented by a standalone structure,
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as implemented in this module.
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The structure is called "KeyedVectors" and is essentially a mapping between *entities*
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and *vectors*. Each entity is identified by its string id, so this is a mapping between {str => 1D numpy array}.
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The entity typically corresponds to a word (so the mapping maps words to 1D vectors),
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but for some models, they key can also correspond to a document, a graph node etc. To generalize
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over different use-cases, this module calls the keys **entities**. Each entity is
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always represented by its string id, no matter whether the entity is a word, a document or a graph node.
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Why use KeyedVectors instead of a full model?
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=============================================
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| capability | KeyedVectors | full model | note |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| continue training vectors | ❌ | ✅ | You need the full model to train or update vectors. |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| smaller objects | ✅ | ❌ | KeyedVectors are smaller and need less RAM, because they |
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| | | | don't need to store the model state that enables training. |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| save/load from native | | | Vectors exported by the Facebook and Google tools |
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| fasttext/word2vec format | ✅ | ❌ | do not support further training, but you can still load |
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| | | | them into KeyedVectors. |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| append new vectors | ✅ | ✅ | Add new entity-vector entries to the mapping dynamically. |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| concurrency | ✅ | ✅ | Thread-safe, allows concurrent vector queries. |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| shared RAM | ✅ | ✅ | Multiple processes can re-use the same data, keeping only |
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| | | | a single copy in RAM using |
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| | | | `mmap <https://en.wikipedia.org/wiki/Mmap>`_. |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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| fast load | ✅ | ✅ | Supports `mmap <https://en.wikipedia.org/wiki/Mmap>`_ |
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| | | | to load data from disk instantaneously. |
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+---------------------------+--------------+------------+-------------------------------------------------------------+
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TL;DR: the main difference is that KeyedVectors do not support further training.
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On the other hand, by shedding the internal data structures necessary for training, KeyedVectors offer a smaller RAM
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footprint and a simpler interface.
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How to obtain word vectors?
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===========================
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Train a full model, then access its `model.wv` property, which holds the standalone keyed vectors.
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For example, using the Word2Vec algorithm to train the vectors
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>>> from gensim.test.utils import common_texts
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>>> from gensim.models import Word2Vec
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>>>
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>>> model = Word2Vec(common_texts, size=100, window=5, min_count=1, workers=4)
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>>> word_vectors = model.wv
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Persist the word vectors to disk with
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>>> from gensim.test.utils import get_tmpfile
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>>> from gensim.models import KeyedVectors
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>>>
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>>> fname = get_tmpfile("vectors.kv")
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>>> word_vectors.save(fname)
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>>> word_vectors = KeyedVectors.load(fname, mmap='r')
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The vectors can also be instantiated from an existing file on disk
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in the original Google's word2vec C format as a KeyedVectors instance
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>>> from gensim.test.utils import datapath
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>>>
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>>> wv_from_text = KeyedVectors.load_word2vec_format(datapath('word2vec_pre_kv_c'), binary=False) # C text format
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>>> wv_from_bin = KeyedVectors.load_word2vec_format(datapath("euclidean_vectors.bin"), binary=True) # C binary format
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What can I do with word vectors?
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================================
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You can perform various syntactic/semantic NLP word tasks with the trained vectors.
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Some of them are already built-in
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>>> import gensim.downloader as api
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>>>
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>>> word_vectors = api.load("glove-wiki-gigaword-100") # load pre-trained word-vectors from gensim-data
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>>>
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>>> result = word_vectors.most_similar(positive=['woman', 'king'], negative=['man'])
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>>> print("{}: {:.4f}".format(*result[0]))
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queen: 0.7699
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>>>
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>>> result = word_vectors.most_similar_cosmul(positive=['woman', 'king'], negative=['man'])
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>>> print("{}: {:.4f}".format(*result[0]))
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queen: 0.8965
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>>>
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>>> print(word_vectors.doesnt_match("breakfast cereal dinner lunch".split()))
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cereal
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>>>
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>>> similarity = word_vectors.similarity('woman', 'man')
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>>> similarity > 0.8
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True
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>>>
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>>> result = word_vectors.similar_by_word("cat")
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>>> print("{}: {:.4f}".format(*result[0]))
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dog: 0.8798
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>>>
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>>> sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()
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>>> sentence_president = 'The president greets the press in Chicago'.lower().split()
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>>>
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>>> similarity = word_vectors.wmdistance(sentence_obama, sentence_president)
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>>> print("{:.4f}".format(similarity))
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3.4893
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>>>
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>>> distance = word_vectors.distance("media", "media")
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>>> print("{:.1f}".format(distance))
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0.0
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>>>
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>>> sim = word_vectors.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant'])
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>>> print("{:.4f}".format(sim))
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0.7067
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>>>
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>>> vector = word_vectors['computer'] # numpy vector of a word
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>>> vector.shape
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(100,)
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>>>
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>>> vector = word_vectors.wv.word_vec('office', use_norm=True)
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>>> vector.shape
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(100,)
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Correlation with human opinion on word similarity
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>>> from gensim.test.utils import datapath
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>>>
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>>> similarities = model.wv.evaluate_word_pairs(datapath('wordsim353.tsv'))
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And on word analogies
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>>> analogy_scores = model.wv.accuracy(datapath('questions-words.txt'))
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and so on.
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"""
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from __future__ import division # py3 "true division"
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import logging
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try:
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from queue import Queue, Empty
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except ImportError:
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from Queue import Queue, Empty # noqa:F401
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# If pyemd C extension is available, import it.
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# If pyemd is attempted to be used, but isn't installed, ImportError will be raised in wmdistance
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try:
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from pyemd import emd
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PYEMD_EXT = True
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except ImportError:
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PYEMD_EXT = False
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from numpy import dot, float32 as REAL, empty, memmap as np_memmap, \
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double, array, zeros, vstack, sqrt, newaxis, integer, \
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ndarray, sum as np_sum, prod, argmax, divide as np_divide
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import numpy as np
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from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc
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from gensim.corpora.dictionary import Dictionary
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from six import string_types, integer_types
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from six.moves import xrange, zip
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from scipy import sparse, stats
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from gensim.utils import deprecated
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from gensim.models.utils_any2vec import _save_word2vec_format, _load_word2vec_format, _compute_ngrams, _ft_hash
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logger = logging.getLogger(__name__)
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class Vocab(object):
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"""A single vocabulary item, used internally for collecting per-word frequency/sampling info,
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and for constructing binary trees (incl. both word leaves and inner nodes).
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"""
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def __init__(self, **kwargs):
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self.count = 0
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self.__dict__.update(kwargs)
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def __lt__(self, other): # used for sorting in a priority queue
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return self.count < other.count
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def __str__(self):
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vals = ['%s:%r' % (key, self.__dict__[key]) for key in sorted(self.__dict__) if not key.startswith('_')]
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return "%s(%s)" % (self.__class__.__name__, ', '.join(vals))
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class BaseKeyedVectors(utils.SaveLoad):
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"""Abstract base class / interface for various types of word vectors."""
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def __init__(self, vector_size):
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self.vectors = zeros((0, vector_size))
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self.vocab = {}
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self.vector_size = vector_size
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self.index2entity = []
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def save(self, fname_or_handle, **kwargs):
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super(BaseKeyedVectors, self).save(fname_or_handle, **kwargs)
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@classmethod
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def load(cls, fname_or_handle, **kwargs):
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return super(BaseKeyedVectors, cls).load(fname_or_handle, **kwargs)
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def similarity(self, entity1, entity2):
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"""Compute cosine similarity between two entities, specified by their string id."""
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raise NotImplementedError()
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def most_similar(self, **kwargs):
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"""Find the top-N most similar entities.
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Possibly have `positive` and `negative` list of entities in `**kwargs`.
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"""
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return NotImplementedError()
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def distance(self, entity1, entity2):
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"""Compute distance between vectors of two input entities, specified by their string id."""
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raise NotImplementedError()
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def distances(self, entity1, other_entities=()):
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"""Compute distances from a given entity (its string id) to all entities in `other_entity`.
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If `other_entities` is empty, return the distance between `entity1` and all entities in vocab.
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"""
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raise NotImplementedError()
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def get_vector(self, entity):
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"""Get the entity's representations in vector space, as a 1D numpy array.
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Parameters
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----------
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entity : str
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Identifier of the entity to return the vector for.
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Returns
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-------
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numpy.ndarray
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Vector for the specified entity.
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Raises
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------
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KeyError
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If the given entity identifier doesn't exist.
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"""
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if entity in self.vocab:
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result = self.vectors[self.vocab[entity].index]
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result.setflags(write=False)
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return result
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else:
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raise KeyError("'%s' not in vocabulary" % entity)
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def add(self, entities, weights, replace=False):
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"""Append entities and theirs vectors in a manual way.
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If some entity is already in the vocabulary, the old vector is kept unless `replace` flag is True.
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Parameters
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----------
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entities : list of str
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Entities specified by string ids.
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weights: {list of numpy.ndarray, numpy.ndarray}
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List of 1D np.array vectors or a 2D np.array of vectors.
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replace: bool, optional
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Flag indicating whether to replace vectors for entities which already exist in the vocabulary,
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if True - replace vectors, otherwise - keep old vectors.
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"""
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if isinstance(entities, string_types):
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entities = [entities]
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weights = np.array(weights).reshape(1, -1)
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elif isinstance(weights, list):
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weights = np.array(weights)
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in_vocab_mask = np.zeros(len(entities), dtype=np.bool)
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for idx, entity in enumerate(entities):
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if entity in self.vocab:
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in_vocab_mask[idx] = True
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# add new entities to the vocab
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for idx in np.nonzero(~in_vocab_mask)[0]:
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entity = entities[idx]
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self.vocab[entity] = Vocab(index=len(self.vocab), count=1)
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self.index2entity.append(entity)
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# add vectors for new entities
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self.vectors = vstack((self.vectors, weights[~in_vocab_mask]))
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# change vectors for in_vocab entities if `replace` flag is specified
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if replace:
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in_vocab_idxs = [self.vocab[entities[idx]].index for idx in np.nonzero(in_vocab_mask)[0]]
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self.vectors[in_vocab_idxs] = weights[in_vocab_mask]
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def __setitem__(self, entities, weights):
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"""Add entities and theirs vectors in a manual way.
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If some entity is already in the vocabulary, old vector is replaced with the new one.
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This method is alias for :meth:`~gensim.models.keyedvectors.BaseKeyedVectors.add` with `replace=True`.
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Parameters
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----------
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entities : {str, list of str}
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Entities specified by their string ids.
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weights: {list of numpy.ndarray, numpy.ndarray}
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List of 1D np.array vectors or 2D np.array of vectors.
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"""
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if not isinstance(entities, list):
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entities = [entities]
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weights = weights.reshape(1, -1)
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self.add(entities, weights, replace=True)
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def __getitem__(self, entities):
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"""Get vector representation of `entities`.
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Parameters
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----------
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entities : {str, list of str}
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Input entity/entities.
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Returns
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-------
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numpy.ndarray
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Vector representation for `entities` (1D if `entities` is string, otherwise - 2D).
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"""
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if isinstance(entities, string_types):
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# allow calls like trained_model['office'], as a shorthand for trained_model[['office']]
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return self.get_vector(entities)
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return vstack([self.get_vector(entity) for entity in entities])
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def __contains__(self, entity):
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return entity in self.vocab
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def most_similar_to_given(self, entity1, entities_list):
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"""Get the `entity` from `entities_list` most similar to `entity1`."""
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return entities_list[argmax([self.similarity(entity1, entity) for entity in entities_list])]
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def closer_than(self, entity1, entity2):
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"""Get all entities that are closer to `entity1` than `entity2` is to `entity1`."""
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all_distances = self.distances(entity1)
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e1_index = self.vocab[entity1].index
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e2_index = self.vocab[entity2].index
|
||
|
|
closer_node_indices = np.where(all_distances < all_distances[e2_index])[0]
|
||
|
|
return [self.index2entity[index] for index in closer_node_indices if index != e1_index]
|
||
|
|
|
||
|
|
def rank(self, entity1, entity2):
|
||
|
|
"""Rank of the distance of `entity2` from `entity1`, in relation to distances of all entities from `entity1`."""
|
||
|
|
return len(self.closer_than(entity1, entity2)) + 1
|
||
|
|
|
||
|
|
|
||
|
|
class WordEmbeddingsKeyedVectors(BaseKeyedVectors):
|
||
|
|
"""Class containing common methods for operations over word vectors."""
|
||
|
|
def __init__(self, vector_size):
|
||
|
|
super(WordEmbeddingsKeyedVectors, self).__init__(vector_size=vector_size)
|
||
|
|
self.vectors_norm = None
|
||
|
|
self.index2word = []
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self instead")
|
||
|
|
def wv(self):
|
||
|
|
return self
|
||
|
|
|
||
|
|
@property
|
||
|
|
def index2entity(self):
|
||
|
|
return self.index2word
|
||
|
|
|
||
|
|
@index2entity.setter
|
||
|
|
def index2entity(self, value):
|
||
|
|
self.index2word = value
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors instead")
|
||
|
|
def syn0(self):
|
||
|
|
return self.vectors
|
||
|
|
|
||
|
|
@syn0.setter
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors instead")
|
||
|
|
def syn0(self, value):
|
||
|
|
self.vectors = value
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors_norm instead")
|
||
|
|
def syn0norm(self):
|
||
|
|
return self.vectors_norm
|
||
|
|
|
||
|
|
@syn0norm.setter
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors_norm instead")
|
||
|
|
def syn0norm(self, value):
|
||
|
|
self.vectors_norm = value
|
||
|
|
|
||
|
|
def __contains__(self, word):
|
||
|
|
return word in self.vocab
|
||
|
|
|
||
|
|
def save(self, *args, **kwargs):
|
||
|
|
"""Save KeyedVectors.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : str
|
||
|
|
Path to the output file.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.load`
|
||
|
|
Load saved model.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# don't bother storing the cached normalized vectors
|
||
|
|
kwargs['ignore'] = kwargs.get('ignore', ['vectors_norm'])
|
||
|
|
super(WordEmbeddingsKeyedVectors, self).save(*args, **kwargs)
|
||
|
|
|
||
|
|
def word_vec(self, word, use_norm=False):
|
||
|
|
"""Get `word` representations in vector space, as a 1D numpy array.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
word : str
|
||
|
|
Input word
|
||
|
|
use_norm : bool, optional
|
||
|
|
If True - resulting vector will be L2-normalized (unit euclidean length).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
Vector representation of `word`.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
KeyError
|
||
|
|
If word not in vocabulary.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if word in self.vocab:
|
||
|
|
if use_norm:
|
||
|
|
result = self.vectors_norm[self.vocab[word].index]
|
||
|
|
else:
|
||
|
|
result = self.vectors[self.vocab[word].index]
|
||
|
|
|
||
|
|
result.setflags(write=False)
|
||
|
|
return result
|
||
|
|
else:
|
||
|
|
raise KeyError("word '%s' not in vocabulary" % word)
|
||
|
|
|
||
|
|
def get_vector(self, word):
|
||
|
|
return self.word_vec(word)
|
||
|
|
|
||
|
|
def words_closer_than(self, w1, w2):
|
||
|
|
"""Get all words that are closer to `w1` than `w2` is to `w1`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
w1 : str
|
||
|
|
Input word.
|
||
|
|
w2 : str
|
||
|
|
Input word.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list (str)
|
||
|
|
List of words that are closer to `w1` than `w2` is to `w1`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return super(WordEmbeddingsKeyedVectors, self).closer_than(w1, w2)
|
||
|
|
|
||
|
|
def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=None, indexer=None):
|
||
|
|
"""Find the top-N most similar words.
|
||
|
|
Positive words contribute positively towards the similarity, negative words negatively.
|
||
|
|
|
||
|
|
This method computes cosine similarity between a simple mean of the projection
|
||
|
|
weight vectors of the given words and the vectors for each word in the model.
|
||
|
|
The method corresponds to the `word-analogy` and `distance` scripts in the original
|
||
|
|
word2vec implementation.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
positive : list of str, optional
|
||
|
|
List of words that contribute positively.
|
||
|
|
negative : list of str, optional
|
||
|
|
List of words that contribute negatively.
|
||
|
|
topn : int, optional
|
||
|
|
Number of top-N similar words to return.
|
||
|
|
restrict_vocab : int, optional
|
||
|
|
Optional integer which limits the range of vectors which
|
||
|
|
are searched for most-similar values. For example, restrict_vocab=10000 would
|
||
|
|
only check the first 10000 word vectors in the vocabulary order. (This may be
|
||
|
|
meaningful if you've sorted the vocabulary by descending frequency.)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list of (str, float)
|
||
|
|
Sequence of (word, similarity).
|
||
|
|
|
||
|
|
"""
|
||
|
|
if positive is None:
|
||
|
|
positive = []
|
||
|
|
if negative is None:
|
||
|
|
negative = []
|
||
|
|
|
||
|
|
self.init_sims()
|
||
|
|
|
||
|
|
if isinstance(positive, string_types) and not negative:
|
||
|
|
# allow calls like most_similar('dog'), as a shorthand for most_similar(['dog'])
|
||
|
|
positive = [positive]
|
||
|
|
|
||
|
|
# add weights for each word, if not already present; default to 1.0 for positive and -1.0 for negative words
|
||
|
|
positive = [
|
||
|
|
(word, 1.0) if isinstance(word, string_types + (ndarray,)) else word
|
||
|
|
for word in positive
|
||
|
|
]
|
||
|
|
negative = [
|
||
|
|
(word, -1.0) if isinstance(word, string_types + (ndarray,)) else word
|
||
|
|
for word in negative
|
||
|
|
]
|
||
|
|
|
||
|
|
# compute the weighted average of all words
|
||
|
|
all_words, mean = set(), []
|
||
|
|
for word, weight in positive + negative:
|
||
|
|
if isinstance(word, ndarray):
|
||
|
|
mean.append(weight * word)
|
||
|
|
else:
|
||
|
|
mean.append(weight * self.word_vec(word, use_norm=True))
|
||
|
|
if word in self.vocab:
|
||
|
|
all_words.add(self.vocab[word].index)
|
||
|
|
if not mean:
|
||
|
|
raise ValueError("cannot compute similarity with no input")
|
||
|
|
mean = matutils.unitvec(array(mean).mean(axis=0)).astype(REAL)
|
||
|
|
|
||
|
|
if indexer is not None:
|
||
|
|
return indexer.most_similar(mean, topn)
|
||
|
|
|
||
|
|
limited = self.vectors_norm if restrict_vocab is None else self.vectors_norm[:restrict_vocab]
|
||
|
|
dists = dot(limited, mean)
|
||
|
|
if not topn:
|
||
|
|
return dists
|
||
|
|
best = matutils.argsort(dists, topn=topn + len(all_words), reverse=True)
|
||
|
|
# ignore (don't return) words from the input
|
||
|
|
result = [(self.index2word[sim], float(dists[sim])) for sim in best if sim not in all_words]
|
||
|
|
return result[:topn]
|
||
|
|
|
||
|
|
def similar_by_word(self, word, topn=10, restrict_vocab=None):
|
||
|
|
"""Find the top-N most similar words.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
word : str
|
||
|
|
Word
|
||
|
|
topn : {int, False}, optional
|
||
|
|
Number of top-N similar words to return. If topn is False, similar_by_word returns
|
||
|
|
the vector of similarity scores.
|
||
|
|
restrict_vocab : int, optional
|
||
|
|
Optional integer which limits the range of vectors which
|
||
|
|
are searched for most-similar values. For example, restrict_vocab=10000 would
|
||
|
|
only check the first 10000 word vectors in the vocabulary order. (This may be
|
||
|
|
meaningful if you've sorted the vocabulary by descending frequency.)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list of (str, float)
|
||
|
|
Sequence of (word, similarity).
|
||
|
|
|
||
|
|
"""
|
||
|
|
return self.most_similar(positive=[word], topn=topn, restrict_vocab=restrict_vocab)
|
||
|
|
|
||
|
|
def similar_by_vector(self, vector, topn=10, restrict_vocab=None):
|
||
|
|
"""Find the top-N most similar words by vector.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
vector : numpy.array
|
||
|
|
Vector from which similarities are to be computed.
|
||
|
|
topn : {int, False}, optional
|
||
|
|
Number of top-N similar words to return. If topn is False, similar_by_vector returns
|
||
|
|
the vector of similarity scores.
|
||
|
|
restrict_vocab : int, optional
|
||
|
|
Optional integer which limits the range of vectors which
|
||
|
|
are searched for most-similar values. For example, restrict_vocab=10000 would
|
||
|
|
only check the first 10000 word vectors in the vocabulary order. (This may be
|
||
|
|
meaningful if you've sorted the vocabulary by descending frequency.)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list of (str, float)
|
||
|
|
Sequence of (word, similarity).
|
||
|
|
|
||
|
|
"""
|
||
|
|
return self.most_similar(positive=[vector], topn=topn, restrict_vocab=restrict_vocab)
|
||
|
|
|
||
|
|
def similarity_matrix(self, dictionary, tfidf=None, threshold=0.0, exponent=2.0, nonzero_limit=100, dtype=REAL):
|
||
|
|
"""Construct a term similarity matrix for computing Soft Cosine Measure.
|
||
|
|
|
||
|
|
This creates a sparse term similarity matrix in the :class:`scipy.sparse.csc_matrix` format for computing
|
||
|
|
Soft Cosine Measure between documents.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
dictionary : :class:`~gensim.corpora.dictionary.Dictionary`
|
||
|
|
A dictionary that specifies a mapping between words and the indices of rows and columns
|
||
|
|
of the resulting term similarity matrix.
|
||
|
|
tfidf : :class:`gensim.models.tfidfmodel.TfidfModel`, optional
|
||
|
|
A model that specifies the relative importance of the terms in the dictionary. The rows
|
||
|
|
of the term similarity matrix will be build in a decreasing order of importance of terms,
|
||
|
|
or in the order of term identifiers if None.
|
||
|
|
threshold : float, optional
|
||
|
|
Only pairs of words whose embeddings are more similar than `threshold` are considered
|
||
|
|
when building the sparse term similarity matrix.
|
||
|
|
exponent : float, optional
|
||
|
|
The exponent applied to the similarity between two word embeddings when building the term similarity matrix.
|
||
|
|
nonzero_limit : int, optional
|
||
|
|
The maximum number of non-zero elements outside the diagonal in a single row or column
|
||
|
|
of the term similarity matrix. Setting `nonzero_limit` to a constant ensures that the
|
||
|
|
time complexity of computing the Soft Cosine Measure will be linear in the document
|
||
|
|
length rather than quadratic.
|
||
|
|
dtype : numpy.dtype, optional
|
||
|
|
Data-type of the term similarity matrix.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
:class:`scipy.sparse.csc_matrix`
|
||
|
|
Term similarity matrix.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:func:`gensim.matutils.softcossim`
|
||
|
|
The Soft Cosine Measure.
|
||
|
|
:class:`~gensim.similarities.docsim.SoftCosineSimilarity`
|
||
|
|
A class for performing corpus-based similarity queries with Soft Cosine Measure.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The constructed matrix corresponds to the matrix Mrel defined in section 2.1 of
|
||
|
|
`Delphine Charlet and Geraldine Damnati, "SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity
|
||
|
|
between Questions for Community Question Answering", 2017
|
||
|
|
<http://www.aclweb.org/anthology/S/S17/S17-2051.pdf>`_.
|
||
|
|
|
||
|
|
"""
|
||
|
|
logger.info("constructing a term similarity matrix")
|
||
|
|
matrix_order = len(dictionary)
|
||
|
|
matrix_nonzero = [1] * matrix_order
|
||
|
|
matrix = sparse.identity(matrix_order, dtype=dtype, format="dok")
|
||
|
|
num_skipped = 0
|
||
|
|
# Decide the order of rows.
|
||
|
|
if tfidf is None:
|
||
|
|
word_indices = range(matrix_order)
|
||
|
|
else:
|
||
|
|
assert max(tfidf.idfs) < matrix_order
|
||
|
|
word_indices = [
|
||
|
|
index for index, _
|
||
|
|
in sorted(tfidf.idfs.items(), key=lambda x: (x[1], -x[0]), reverse=True)
|
||
|
|
]
|
||
|
|
|
||
|
|
# Traverse rows.
|
||
|
|
for row_number, w1_index in enumerate(word_indices):
|
||
|
|
if row_number % 1000 == 0:
|
||
|
|
logger.info(
|
||
|
|
"PROGRESS: at %.02f%% rows (%d / %d, %d skipped, %.06f%% density)",
|
||
|
|
100.0 * (row_number + 1) / matrix_order, row_number + 1, matrix_order,
|
||
|
|
num_skipped, 100.0 * matrix.getnnz() / matrix_order**2)
|
||
|
|
w1 = dictionary[w1_index]
|
||
|
|
if w1 not in self.vocab:
|
||
|
|
num_skipped += 1
|
||
|
|
continue # A word from the dictionary is not present in the word2vec model.
|
||
|
|
|
||
|
|
# Traverse upper triangle columns.
|
||
|
|
if matrix_order <= nonzero_limit + 1: # Traverse all columns.
|
||
|
|
columns = (
|
||
|
|
(w2_index, self.similarity(w1, dictionary[w2_index]))
|
||
|
|
for w2_index in range(w1_index + 1, matrix_order)
|
||
|
|
if w1_index != w2_index and dictionary[w2_index] in self.vocab)
|
||
|
|
else: # Traverse only columns corresponding to the embeddings closest to w1.
|
||
|
|
num_nonzero = matrix_nonzero[w1_index] - 1
|
||
|
|
columns = (
|
||
|
|
(dictionary.token2id[w2], similarity)
|
||
|
|
for _, (w2, similarity)
|
||
|
|
in zip(
|
||
|
|
range(nonzero_limit - num_nonzero),
|
||
|
|
self.most_similar(positive=[w1], topn=nonzero_limit - num_nonzero)
|
||
|
|
)
|
||
|
|
if w2 in dictionary.token2id
|
||
|
|
)
|
||
|
|
columns = sorted(columns, key=lambda x: x[0])
|
||
|
|
|
||
|
|
for w2_index, similarity in columns:
|
||
|
|
# Ensure that we don't exceed `nonzero_limit` by mirroring the upper triangle.
|
||
|
|
if similarity > threshold and matrix_nonzero[w2_index] <= nonzero_limit:
|
||
|
|
element = similarity**exponent
|
||
|
|
matrix[w1_index, w2_index] = element
|
||
|
|
matrix_nonzero[w1_index] += 1
|
||
|
|
matrix[w2_index, w1_index] = element
|
||
|
|
matrix_nonzero[w2_index] += 1
|
||
|
|
logger.info(
|
||
|
|
"constructed a term similarity matrix with %0.6f %% nonzero elements",
|
||
|
|
100.0 * matrix.getnnz() / matrix_order**2
|
||
|
|
)
|
||
|
|
return matrix.tocsc()
|
||
|
|
|
||
|
|
def wmdistance(self, document1, document2):
|
||
|
|
"""Compute the Word Mover's Distance between two documents.
|
||
|
|
|
||
|
|
When using this code, please consider citing the following papers:
|
||
|
|
|
||
|
|
* `Ofir Pele and Michael Werman "A linear time histogram metric for improved SIFT matching"
|
||
|
|
<http://www.cs.huji.ac.il/~werman/Papers/ECCV2008.pdf>`_
|
||
|
|
* `Ofir Pele and Michael Werman "Fast and robust earth mover's distances"
|
||
|
|
<https://ieeexplore.ieee.org/document/5459199/>`_
|
||
|
|
* `Matt Kusner et al. "From Word Embeddings To Document Distances"
|
||
|
|
<http://proceedings.mlr.press/v37/kusnerb15.pdf>`_.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
document1 : list of str
|
||
|
|
Input document.
|
||
|
|
document2 : list of str
|
||
|
|
Input document.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
float
|
||
|
|
Word Mover's distance between `document1` and `document2`.
|
||
|
|
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
This method only works if `pyemd <https://pypi.org/project/pyemd/>`_ is installed.
|
||
|
|
|
||
|
|
If one of the documents have no words that exist in the vocab, `float('inf')` (i.e. infinity)
|
||
|
|
will be returned.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ImportError
|
||
|
|
If `pyemd <https://pypi.org/project/pyemd/>`_ isn't installed.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not PYEMD_EXT:
|
||
|
|
raise ImportError("Please install pyemd Python package to compute WMD.")
|
||
|
|
|
||
|
|
# Remove out-of-vocabulary words.
|
||
|
|
len_pre_oov1 = len(document1)
|
||
|
|
len_pre_oov2 = len(document2)
|
||
|
|
document1 = [token for token in document1 if token in self]
|
||
|
|
document2 = [token for token in document2 if token in self]
|
||
|
|
diff1 = len_pre_oov1 - len(document1)
|
||
|
|
diff2 = len_pre_oov2 - len(document2)
|
||
|
|
if diff1 > 0 or diff2 > 0:
|
||
|
|
logger.info('Removed %d and %d OOV words from document 1 and 2 (respectively).', diff1, diff2)
|
||
|
|
|
||
|
|
if len(document1) == 0 or len(document2) == 0:
|
||
|
|
logger.info(
|
||
|
|
"At least one of the documents had no words that werein the vocabulary. "
|
||
|
|
"Aborting (returning inf)."
|
||
|
|
)
|
||
|
|
return float('inf')
|
||
|
|
|
||
|
|
dictionary = Dictionary(documents=[document1, document2])
|
||
|
|
vocab_len = len(dictionary)
|
||
|
|
|
||
|
|
if vocab_len == 1:
|
||
|
|
# Both documents are composed by a single unique token
|
||
|
|
return 0.0
|
||
|
|
|
||
|
|
# Sets for faster look-up.
|
||
|
|
docset1 = set(document1)
|
||
|
|
docset2 = set(document2)
|
||
|
|
|
||
|
|
# Compute distance matrix.
|
||
|
|
distance_matrix = zeros((vocab_len, vocab_len), dtype=double)
|
||
|
|
for i, t1 in dictionary.items():
|
||
|
|
for j, t2 in dictionary.items():
|
||
|
|
if t1 not in docset1 or t2 not in docset2:
|
||
|
|
continue
|
||
|
|
# Compute Euclidean distance between word vectors.
|
||
|
|
distance_matrix[i, j] = sqrt(np_sum((self[t1] - self[t2])**2))
|
||
|
|
|
||
|
|
if np_sum(distance_matrix) == 0.0:
|
||
|
|
# `emd` gets stuck if the distance matrix contains only zeros.
|
||
|
|
logger.info('The distance matrix is all zeros. Aborting (returning inf).')
|
||
|
|
return float('inf')
|
||
|
|
|
||
|
|
def nbow(document):
|
||
|
|
d = zeros(vocab_len, dtype=double)
|
||
|
|
nbow = dictionary.doc2bow(document) # Word frequencies.
|
||
|
|
doc_len = len(document)
|
||
|
|
for idx, freq in nbow:
|
||
|
|
d[idx] = freq / float(doc_len) # Normalized word frequencies.
|
||
|
|
return d
|
||
|
|
|
||
|
|
# Compute nBOW representation of documents.
|
||
|
|
d1 = nbow(document1)
|
||
|
|
d2 = nbow(document2)
|
||
|
|
|
||
|
|
# Compute WMD.
|
||
|
|
return emd(d1, d2, distance_matrix)
|
||
|
|
|
||
|
|
def most_similar_cosmul(self, positive=None, negative=None, topn=10):
|
||
|
|
"""Find the top-N most similar words, using the multiplicative combination objective,
|
||
|
|
proposed by `Omer Levy and Yoav Goldberg "Linguistic Regularities in Sparse and Explicit Word Representations"
|
||
|
|
<http://www.aclweb.org/anthology/W14-1618>`_. Positive words still contribute positively towards the similarity,
|
||
|
|
negative words negatively, but with less susceptibility to one large distance dominating the calculation.
|
||
|
|
In the common analogy-solving case, of two positive and one negative examples,
|
||
|
|
this method is equivalent to the "3CosMul" objective (equation (4)) of Levy and Goldberg.
|
||
|
|
|
||
|
|
Additional positive or negative examples contribute to the numerator or denominator,
|
||
|
|
respectively - a potentially sensible but untested extension of the method.
|
||
|
|
With a single positive example, rankings will be the same as in the default
|
||
|
|
:meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.most_similar`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
positive : list of str, optional
|
||
|
|
List of words that contribute positively.
|
||
|
|
negative : list of str, optional
|
||
|
|
List of words that contribute negatively.
|
||
|
|
topn : int, optional
|
||
|
|
Number of top-N similar words to return.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list of (str, float)
|
||
|
|
Sequence of (word, similarity).
|
||
|
|
|
||
|
|
"""
|
||
|
|
if positive is None:
|
||
|
|
positive = []
|
||
|
|
if negative is None:
|
||
|
|
negative = []
|
||
|
|
|
||
|
|
self.init_sims()
|
||
|
|
|
||
|
|
if isinstance(positive, string_types) and not negative:
|
||
|
|
# allow calls like most_similar_cosmul('dog'), as a shorthand for most_similar_cosmul(['dog'])
|
||
|
|
positive = [positive]
|
||
|
|
|
||
|
|
all_words = {
|
||
|
|
self.vocab[word].index for word in positive + negative
|
||
|
|
if not isinstance(word, ndarray) and word in self.vocab
|
||
|
|
}
|
||
|
|
|
||
|
|
positive = [
|
||
|
|
self.word_vec(word, use_norm=True) if isinstance(word, string_types) else word
|
||
|
|
for word in positive
|
||
|
|
]
|
||
|
|
negative = [
|
||
|
|
self.word_vec(word, use_norm=True) if isinstance(word, string_types) else word
|
||
|
|
for word in negative
|
||
|
|
]
|
||
|
|
|
||
|
|
if not positive:
|
||
|
|
raise ValueError("cannot compute similarity with no input")
|
||
|
|
|
||
|
|
# equation (4) of Levy & Goldberg "Linguistic Regularities...",
|
||
|
|
# with distances shifted to [0,1] per footnote (7)
|
||
|
|
pos_dists = [((1 + dot(self.vectors_norm, term)) / 2) for term in positive]
|
||
|
|
neg_dists = [((1 + dot(self.vectors_norm, term)) / 2) for term in negative]
|
||
|
|
dists = prod(pos_dists, axis=0) / (prod(neg_dists, axis=0) + 0.000001)
|
||
|
|
|
||
|
|
if not topn:
|
||
|
|
return dists
|
||
|
|
best = matutils.argsort(dists, topn=topn + len(all_words), reverse=True)
|
||
|
|
# ignore (don't return) words from the input
|
||
|
|
result = [(self.index2word[sim], float(dists[sim])) for sim in best if sim not in all_words]
|
||
|
|
return result[:topn]
|
||
|
|
|
||
|
|
def doesnt_match(self, words):
|
||
|
|
"""Which word from the given list doesn't go with the others?
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
words : list of str
|
||
|
|
List of words.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
str
|
||
|
|
The word further away from the mean of all words.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.init_sims()
|
||
|
|
|
||
|
|
used_words = [word for word in words if word in self]
|
||
|
|
if len(used_words) != len(words):
|
||
|
|
ignored_words = set(words) - set(used_words)
|
||
|
|
logger.warning("vectors for words %s are not present in the model, ignoring these words", ignored_words)
|
||
|
|
if not used_words:
|
||
|
|
raise ValueError("cannot select a word from an empty list")
|
||
|
|
vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)
|
||
|
|
mean = matutils.unitvec(vectors.mean(axis=0)).astype(REAL)
|
||
|
|
dists = dot(vectors, mean)
|
||
|
|
return sorted(zip(dists, used_words))[0][1]
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def cosine_similarities(vector_1, vectors_all):
|
||
|
|
"""Compute cosine similarities between one vector and a set of other vectors.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
vector_1 : numpy.ndarray
|
||
|
|
Vector from which similarities are to be computed, expected shape (dim,).
|
||
|
|
vectors_all : numpy.ndarray
|
||
|
|
For each row in vectors_all, distance from vector_1 is computed, expected shape (num_vectors, dim).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
Contains cosine distance between `vector_1` and each row in `vectors_all`, shape (num_vectors,).
|
||
|
|
|
||
|
|
"""
|
||
|
|
norm = np.linalg.norm(vector_1)
|
||
|
|
all_norms = np.linalg.norm(vectors_all, axis=1)
|
||
|
|
dot_products = dot(vectors_all, vector_1)
|
||
|
|
similarities = dot_products / (norm * all_norms)
|
||
|
|
return similarities
|
||
|
|
|
||
|
|
def distances(self, word_or_vector, other_words=()):
|
||
|
|
"""Compute cosine distances from given word or vector to all words in `other_words`.
|
||
|
|
If `other_words` is empty, return distance between `word_or_vectors` and all words in vocab.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
word_or_vector : {str, numpy.ndarray}
|
||
|
|
Word or vector from which distances are to be computed.
|
||
|
|
other_words : iterable of str
|
||
|
|
For each word in `other_words` distance from `word_or_vector` is computed.
|
||
|
|
If None or empty, distance of `word_or_vector` from all words in vocab is computed (including itself).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.array
|
||
|
|
Array containing distances to all words in `other_words` from input `word_or_vector`.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
-----
|
||
|
|
KeyError
|
||
|
|
If either `word_or_vector` or any word in `other_words` is absent from vocab.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if isinstance(word_or_vector, string_types):
|
||
|
|
input_vector = self.word_vec(word_or_vector)
|
||
|
|
else:
|
||
|
|
input_vector = word_or_vector
|
||
|
|
if not other_words:
|
||
|
|
other_vectors = self.vectors
|
||
|
|
else:
|
||
|
|
other_indices = [self.vocab[word].index for word in other_words]
|
||
|
|
other_vectors = self.vectors[other_indices]
|
||
|
|
return 1 - self.cosine_similarities(input_vector, other_vectors)
|
||
|
|
|
||
|
|
def distance(self, w1, w2):
|
||
|
|
"""Compute cosine distance between two words.
|
||
|
|
Calculate 1 - :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similarity`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
w1 : str
|
||
|
|
Input word.
|
||
|
|
w2 : str
|
||
|
|
Input word.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
float
|
||
|
|
Distance between `w1` and `w2`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return 1 - self.similarity(w1, w2)
|
||
|
|
|
||
|
|
def similarity(self, w1, w2):
|
||
|
|
"""Compute cosine similarity between two words.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
w1 : str
|
||
|
|
Input word.
|
||
|
|
w2 : str
|
||
|
|
Input word.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
float
|
||
|
|
Cosine similarity between `w1` and `w2`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return dot(matutils.unitvec(self[w1]), matutils.unitvec(self[w2]))
|
||
|
|
|
||
|
|
def n_similarity(self, ws1, ws2):
|
||
|
|
"""Compute cosine similarity between two sets of words.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
ws1 : list of str
|
||
|
|
Sequence of words.
|
||
|
|
ws2: list of str
|
||
|
|
Sequence of words.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
Similarities between `ws1` and `ws2`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if not(len(ws1) and len(ws2)):
|
||
|
|
raise ZeroDivisionError('At least one of the passed list is empty.')
|
||
|
|
v1 = [self[word] for word in ws1]
|
||
|
|
v2 = [self[word] for word in ws2]
|
||
|
|
return dot(matutils.unitvec(array(v1).mean(axis=0)), matutils.unitvec(array(v2).mean(axis=0)))
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _log_evaluate_word_analogies(section):
|
||
|
|
"""Calculate score by section, helper for
|
||
|
|
:meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.evaluate_word_analogies`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
section : dict of (str, (str, str, str, str))
|
||
|
|
Section given from evaluation.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
float
|
||
|
|
Accuracy score.
|
||
|
|
|
||
|
|
"""
|
||
|
|
correct, incorrect = len(section['correct']), len(section['incorrect'])
|
||
|
|
if correct + incorrect > 0:
|
||
|
|
score = correct / (correct + incorrect)
|
||
|
|
logger.info("%s: %.1f%% (%i/%i)", section['section'], 100.0 * score, correct, correct + incorrect)
|
||
|
|
return score
|
||
|
|
|
||
|
|
def evaluate_word_analogies(self, analogies, restrict_vocab=300000, case_insensitive=True, dummy4unknown=False):
|
||
|
|
"""Compute performance of the model on an analogy test set.
|
||
|
|
|
||
|
|
This is modern variant of :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.accuracy`, see
|
||
|
|
`discussion on GitHub #1935 <https://github.com/RaRe-Technologies/gensim/pull/1935>`_.
|
||
|
|
|
||
|
|
The accuracy is reported (printed to log and returned as a score) for each section separately,
|
||
|
|
plus there's one aggregate summary at the end.
|
||
|
|
|
||
|
|
This method corresponds to the `compute-accuracy` script of the original C word2vec.
|
||
|
|
See also `Analogy (State of the art) <https://aclweb.org/aclwiki/Analogy_(State_of_the_art)>`_.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
analogies : str
|
||
|
|
Path to file, where lines are 4-tuples of words, split into sections by ": SECTION NAME" lines.
|
||
|
|
See `gensim/test/test_data/questions-words.txt` as example.
|
||
|
|
restrict_vocab : int, optional
|
||
|
|
Ignore all 4-tuples containing a word not in the first `restrict_vocab` words.
|
||
|
|
This may be meaningful if you've sorted the model vocabulary by descending frequency (which is standard
|
||
|
|
in modern word embedding models).
|
||
|
|
case_insensitive : bool, optional
|
||
|
|
If True - convert all words to their uppercase form before evaluating the performance.
|
||
|
|
Useful to handle case-mismatch between training tokens and words in the test set.
|
||
|
|
In case of multiple case variants of a single word, the vector for the first occurrence
|
||
|
|
(also the most frequent if vocabulary is sorted) is taken.
|
||
|
|
dummy4unknown : bool, optional
|
||
|
|
If True - produce zero accuracies for 4-tuples with out-of-vocabulary words.
|
||
|
|
Otherwise, these tuples are skipped entirely and not used in the evaluation.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
(float, list of dict of (str, (str, str, str))
|
||
|
|
Overall evaluation score and full lists of correct and incorrect predictions divided by sections.
|
||
|
|
|
||
|
|
"""
|
||
|
|
ok_vocab = [(w, self.vocab[w]) for w in self.index2word[:restrict_vocab]]
|
||
|
|
ok_vocab = {w.upper(): v for w, v in reversed(ok_vocab)} if case_insensitive else dict(ok_vocab)
|
||
|
|
oov = 0
|
||
|
|
logger.info("Evaluating word analogies for top %i words in the model on %s", restrict_vocab, analogies)
|
||
|
|
sections, section = [], None
|
||
|
|
quadruplets_no = 0
|
||
|
|
for line_no, line in enumerate(utils.smart_open(analogies)):
|
||
|
|
line = utils.to_unicode(line)
|
||
|
|
if line.startswith(': '):
|
||
|
|
# a new section starts => store the old section
|
||
|
|
if section:
|
||
|
|
sections.append(section)
|
||
|
|
self._log_evaluate_word_analogies(section)
|
||
|
|
section = {'section': line.lstrip(': ').strip(), 'correct': [], 'incorrect': []}
|
||
|
|
else:
|
||
|
|
if not section:
|
||
|
|
raise ValueError("Missing section header before line #%i in %s" % (line_no, analogies))
|
||
|
|
try:
|
||
|
|
if case_insensitive:
|
||
|
|
a, b, c, expected = [word.upper() for word in line.split()]
|
||
|
|
else:
|
||
|
|
a, b, c, expected = [word for word in line.split()]
|
||
|
|
except ValueError:
|
||
|
|
logger.info("Skipping invalid line #%i in %s", line_no, analogies)
|
||
|
|
continue
|
||
|
|
quadruplets_no += 1
|
||
|
|
if a not in ok_vocab or b not in ok_vocab or c not in ok_vocab or expected not in ok_vocab:
|
||
|
|
oov += 1
|
||
|
|
if dummy4unknown:
|
||
|
|
logger.debug('Zero accuracy for line #%d with OOV words: %s', line_no, line.strip())
|
||
|
|
section['incorrect'].append((a, b, c, expected))
|
||
|
|
else:
|
||
|
|
logger.debug("Skipping line #%i with OOV words: %s", line_no, line.strip())
|
||
|
|
continue
|
||
|
|
original_vocab = self.vocab
|
||
|
|
self.vocab = ok_vocab
|
||
|
|
ignore = {a, b, c} # input words to be ignored
|
||
|
|
predicted = None
|
||
|
|
# find the most likely prediction using 3CosAdd (vector offset) method
|
||
|
|
# TODO: implement 3CosMul and set-based methods for solving analogies
|
||
|
|
sims = self.most_similar(positive=[b, c], negative=[a], topn=5, restrict_vocab=restrict_vocab)
|
||
|
|
self.vocab = original_vocab
|
||
|
|
for element in sims:
|
||
|
|
predicted = element[0].upper() if case_insensitive else element[0]
|
||
|
|
if predicted in ok_vocab and predicted not in ignore:
|
||
|
|
if predicted != expected:
|
||
|
|
logger.debug("%s: expected %s, predicted %s", line.strip(), expected, predicted)
|
||
|
|
break
|
||
|
|
if predicted == expected:
|
||
|
|
section['correct'].append((a, b, c, expected))
|
||
|
|
else:
|
||
|
|
section['incorrect'].append((a, b, c, expected))
|
||
|
|
if section:
|
||
|
|
# store the last section, too
|
||
|
|
sections.append(section)
|
||
|
|
self._log_evaluate_word_analogies(section)
|
||
|
|
|
||
|
|
total = {
|
||
|
|
'section': 'Total accuracy',
|
||
|
|
'correct': sum((s['correct'] for s in sections), []),
|
||
|
|
'incorrect': sum((s['incorrect'] for s in sections), []),
|
||
|
|
}
|
||
|
|
|
||
|
|
oov_ratio = float(oov) / quadruplets_no * 100
|
||
|
|
logger.info('Quadruplets with out-of-vocabulary words: %.1f%%', oov_ratio)
|
||
|
|
if not dummy4unknown:
|
||
|
|
logger.info(
|
||
|
|
'NB: analogies containing OOV words were skipped from evaluation! '
|
||
|
|
'To change this behavior, use "dummy4unknown=True"'
|
||
|
|
)
|
||
|
|
analogies_score = self._log_evaluate_word_analogies(total)
|
||
|
|
sections.append(total)
|
||
|
|
# Return the overall score and the full lists of correct and incorrect analogies
|
||
|
|
return analogies_score, sections
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def log_accuracy(section):
|
||
|
|
correct, incorrect = len(section['correct']), len(section['incorrect'])
|
||
|
|
if correct + incorrect > 0:
|
||
|
|
logger.info(
|
||
|
|
"%s: %.1f%% (%i/%i)",
|
||
|
|
section['section'], 100.0 * correct / (correct + incorrect), correct, correct + incorrect
|
||
|
|
)
|
||
|
|
|
||
|
|
@deprecated("Method will be removed in 4.0.0, use self.evaluate_word_analogies() instead")
|
||
|
|
def accuracy(self, questions, restrict_vocab=30000, most_similar=most_similar, case_insensitive=True):
|
||
|
|
"""Compute accuracy of the model.
|
||
|
|
|
||
|
|
The accuracy is reported (=printed to log and returned as a list) for each
|
||
|
|
section separately, plus there's one aggregate summary at the end.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
questions : str
|
||
|
|
Path to file, where lines are 4-tuples of words, split into sections by ": SECTION NAME" lines.
|
||
|
|
See `gensim/test/test_data/questions-words.txt` as example.
|
||
|
|
restrict_vocab : int, optional
|
||
|
|
Ignore all 4-tuples containing a word not in the first `restrict_vocab` words.
|
||
|
|
This may be meaningful if you've sorted the model vocabulary by descending frequency (which is standard
|
||
|
|
in modern word embedding models).
|
||
|
|
most_similar : function, optional
|
||
|
|
Function used for similarity calculation.
|
||
|
|
case_insensitive : bool, optional
|
||
|
|
If True - convert all words to their uppercase form before evaluating the performance.
|
||
|
|
Useful to handle case-mismatch between training tokens and words in the test set.
|
||
|
|
In case of multiple case variants of a single word, the vector for the first occurrence
|
||
|
|
(also the most frequent if vocabulary is sorted) is taken.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list of dict of (str, (str, str, str)
|
||
|
|
Full lists of correct and incorrect predictions divided by sections.
|
||
|
|
|
||
|
|
"""
|
||
|
|
ok_vocab = [(w, self.vocab[w]) for w in self.index2word[:restrict_vocab]]
|
||
|
|
ok_vocab = {w.upper(): v for w, v in reversed(ok_vocab)} if case_insensitive else dict(ok_vocab)
|
||
|
|
|
||
|
|
sections, section = [], None
|
||
|
|
for line_no, line in enumerate(utils.smart_open(questions)):
|
||
|
|
# TODO: use level3 BLAS (=evaluate multiple questions at once), for speed
|
||
|
|
line = utils.to_unicode(line)
|
||
|
|
if line.startswith(': '):
|
||
|
|
# a new section starts => store the old section
|
||
|
|
if section:
|
||
|
|
sections.append(section)
|
||
|
|
self.log_accuracy(section)
|
||
|
|
section = {'section': line.lstrip(': ').strip(), 'correct': [], 'incorrect': []}
|
||
|
|
else:
|
||
|
|
if not section:
|
||
|
|
raise ValueError("Missing section header before line #%i in %s" % (line_no, questions))
|
||
|
|
try:
|
||
|
|
if case_insensitive:
|
||
|
|
a, b, c, expected = [word.upper() for word in line.split()]
|
||
|
|
else:
|
||
|
|
a, b, c, expected = [word for word in line.split()]
|
||
|
|
except ValueError:
|
||
|
|
logger.info("Skipping invalid line #%i in %s", line_no, questions)
|
||
|
|
continue
|
||
|
|
if a not in ok_vocab or b not in ok_vocab or c not in ok_vocab or expected not in ok_vocab:
|
||
|
|
logger.debug("Skipping line #%i with OOV words: %s", line_no, line.strip())
|
||
|
|
continue
|
||
|
|
original_vocab = self.vocab
|
||
|
|
self.vocab = ok_vocab
|
||
|
|
ignore = {a, b, c} # input words to be ignored
|
||
|
|
predicted = None
|
||
|
|
# find the most likely prediction, ignoring OOV words and input words
|
||
|
|
sims = most_similar(self, positive=[b, c], negative=[a], topn=False, restrict_vocab=restrict_vocab)
|
||
|
|
self.vocab = original_vocab
|
||
|
|
for index in matutils.argsort(sims, reverse=True):
|
||
|
|
predicted = self.index2word[index].upper() if case_insensitive else self.index2word[index]
|
||
|
|
if predicted in ok_vocab and predicted not in ignore:
|
||
|
|
if predicted != expected:
|
||
|
|
logger.debug("%s: expected %s, predicted %s", line.strip(), expected, predicted)
|
||
|
|
break
|
||
|
|
if predicted == expected:
|
||
|
|
section['correct'].append((a, b, c, expected))
|
||
|
|
else:
|
||
|
|
section['incorrect'].append((a, b, c, expected))
|
||
|
|
if section:
|
||
|
|
# store the last section, too
|
||
|
|
sections.append(section)
|
||
|
|
self.log_accuracy(section)
|
||
|
|
|
||
|
|
total = {
|
||
|
|
'section': 'total',
|
||
|
|
'correct': sum((s['correct'] for s in sections), []),
|
||
|
|
'incorrect': sum((s['incorrect'] for s in sections), []),
|
||
|
|
}
|
||
|
|
self.log_accuracy(total)
|
||
|
|
sections.append(total)
|
||
|
|
return sections
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def log_evaluate_word_pairs(pearson, spearman, oov, pairs):
|
||
|
|
logger.info('Pearson correlation coefficient against %s: %.4f', pairs, pearson[0])
|
||
|
|
logger.info('Spearman rank-order correlation coefficient against %s: %.4f', pairs, spearman[0])
|
||
|
|
logger.info('Pairs with unknown words ratio: %.1f%%', oov)
|
||
|
|
|
||
|
|
def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000,
|
||
|
|
case_insensitive=True, dummy4unknown=False):
|
||
|
|
"""Compute correlation of the model with human similarity judgments.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
More datasets can be found at
|
||
|
|
* http://technion.ac.il/~ira.leviant/MultilingualVSMdata.html
|
||
|
|
* https://www.cl.cam.ac.uk/~fh295/simlex.html.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
pairs : str
|
||
|
|
Path to file, where lines are 3-tuples, each consisting of a word pair and a similarity value.
|
||
|
|
See `test/test_data/wordsim353.tsv` as example.
|
||
|
|
delimiter : str, optional
|
||
|
|
Separator in `pairs` file.
|
||
|
|
restrict_vocab : int, optional
|
||
|
|
Ignore all 4-tuples containing a word not in the first `restrict_vocab` words.
|
||
|
|
This may be meaningful if you've sorted the model vocabulary by descending frequency (which is standard
|
||
|
|
in modern word embedding models).
|
||
|
|
case_insensitive : bool, optional
|
||
|
|
If True - convert all words to their uppercase form before evaluating the performance.
|
||
|
|
Useful to handle case-mismatch between training tokens and words in the test set.
|
||
|
|
In case of multiple case variants of a single word, the vector for the first occurrence
|
||
|
|
(also the most frequent if vocabulary is sorted) is taken.
|
||
|
|
dummy4unknown : bool, optional
|
||
|
|
If True - produce zero accuracies for 4-tuples with out-of-vocabulary words.
|
||
|
|
Otherwise, these tuples are skipped entirely and not used in the evaluation.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
(float, float, float)
|
||
|
|
Pearson correlation coefficient, Spearman rank-order correlation coefficient between the similarities
|
||
|
|
from the dataset and the similarities produced by the model itself, ratio of pairs with unknown words.
|
||
|
|
|
||
|
|
"""
|
||
|
|
ok_vocab = [(w, self.vocab[w]) for w in self.index2word[:restrict_vocab]]
|
||
|
|
ok_vocab = {w.upper(): v for w, v in reversed(ok_vocab)} if case_insensitive else dict(ok_vocab)
|
||
|
|
|
||
|
|
similarity_gold = []
|
||
|
|
similarity_model = []
|
||
|
|
oov = 0
|
||
|
|
|
||
|
|
original_vocab = self.vocab
|
||
|
|
self.vocab = ok_vocab
|
||
|
|
|
||
|
|
for line_no, line in enumerate(utils.smart_open(pairs)):
|
||
|
|
line = utils.to_unicode(line)
|
||
|
|
if line.startswith('#'):
|
||
|
|
# May be a comment
|
||
|
|
continue
|
||
|
|
else:
|
||
|
|
try:
|
||
|
|
if case_insensitive:
|
||
|
|
a, b, sim = [word.upper() for word in line.split(delimiter)]
|
||
|
|
else:
|
||
|
|
a, b, sim = [word for word in line.split(delimiter)]
|
||
|
|
sim = float(sim)
|
||
|
|
except (ValueError, TypeError):
|
||
|
|
logger.info('Skipping invalid line #%d in %s', line_no, pairs)
|
||
|
|
continue
|
||
|
|
if a not in ok_vocab or b not in ok_vocab:
|
||
|
|
oov += 1
|
||
|
|
if dummy4unknown:
|
||
|
|
logger.debug('Zero similarity for line #%d with OOV words: %s', line_no, line.strip())
|
||
|
|
similarity_model.append(0.0)
|
||
|
|
similarity_gold.append(sim)
|
||
|
|
continue
|
||
|
|
else:
|
||
|
|
logger.debug('Skipping line #%d with OOV words: %s', line_no, line.strip())
|
||
|
|
continue
|
||
|
|
similarity_gold.append(sim) # Similarity from the dataset
|
||
|
|
similarity_model.append(self.similarity(a, b)) # Similarity from the model
|
||
|
|
self.vocab = original_vocab
|
||
|
|
spearman = stats.spearmanr(similarity_gold, similarity_model)
|
||
|
|
pearson = stats.pearsonr(similarity_gold, similarity_model)
|
||
|
|
if dummy4unknown:
|
||
|
|
oov_ratio = float(oov) / len(similarity_gold) * 100
|
||
|
|
else:
|
||
|
|
oov_ratio = float(oov) / (len(similarity_gold) + oov) * 100
|
||
|
|
|
||
|
|
logger.debug('Pearson correlation coefficient against %s: %f with p-value %f', pairs, pearson[0], pearson[1])
|
||
|
|
logger.debug(
|
||
|
|
'Spearman rank-order correlation coefficient against %s: %f with p-value %f',
|
||
|
|
pairs, spearman[0], spearman[1]
|
||
|
|
)
|
||
|
|
logger.debug('Pairs with unknown words: %d', oov)
|
||
|
|
self.log_evaluate_word_pairs(pearson, spearman, oov_ratio, pairs)
|
||
|
|
return pearson, spearman, oov_ratio
|
||
|
|
|
||
|
|
def init_sims(self, replace=False):
|
||
|
|
"""Precompute L2-normalized vectors.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
replace : bool, optional
|
||
|
|
If True - forget the original vectors and only keep the normalized ones = saves lots of memory!
|
||
|
|
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
You **cannot continue training** after doing a replace.
|
||
|
|
The model becomes effectively read-only: you can call
|
||
|
|
:meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.most_similar`,
|
||
|
|
:meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similarity`, etc., but not train.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if getattr(self, 'vectors_norm', None) is None or replace:
|
||
|
|
logger.info("precomputing L2-norms of word weight vectors")
|
||
|
|
if replace:
|
||
|
|
for i in xrange(self.vectors.shape[0]):
|
||
|
|
self.vectors[i, :] /= sqrt((self.vectors[i, :] ** 2).sum(-1))
|
||
|
|
self.vectors_norm = self.vectors
|
||
|
|
else:
|
||
|
|
self.vectors_norm = (self.vectors / sqrt((self.vectors ** 2).sum(-1))[..., newaxis]).astype(REAL)
|
||
|
|
|
||
|
|
|
||
|
|
class Word2VecKeyedVectors(WordEmbeddingsKeyedVectors):
|
||
|
|
"""Mapping between words and vectors for the :class:`~gensim.models.Word2Vec` model.
|
||
|
|
Used to perform operations on the vectors such as vector lookup, distance, similarity etc.
|
||
|
|
|
||
|
|
"""
|
||
|
|
def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None):
|
||
|
|
"""Store the input-hidden weight matrix in the same format used by the original
|
||
|
|
C word2vec-tool, for compatibility.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : str
|
||
|
|
The file path used to save the vectors in
|
||
|
|
fvocab : str, optional
|
||
|
|
Optional file path used to save the vocabulary
|
||
|
|
binary : bool, optional
|
||
|
|
If True, the data wil be saved in binary word2vec format, else it will be saved in plain text.
|
||
|
|
total_vec : int, optional
|
||
|
|
Optional parameter to explicitly specify total no. of vectors
|
||
|
|
(in case word vectors are appended with document vectors afterwards).
|
||
|
|
|
||
|
|
"""
|
||
|
|
# from gensim.models.word2vec import save_word2vec_format
|
||
|
|
_save_word2vec_format(
|
||
|
|
fname, self.vocab, self.vectors, fvocab=fvocab, binary=binary, total_vec=total_vec)
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict',
|
||
|
|
limit=None, datatype=REAL):
|
||
|
|
"""Load the input-hidden weight matrix from the original C word2vec-tool format.
|
||
|
|
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
The information stored in the file is incomplete (the binary tree is missing),
|
||
|
|
so while you can query for word similarity etc., you cannot continue training
|
||
|
|
with a model loaded this way.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : str
|
||
|
|
The file path to the saved word2vec-format file.
|
||
|
|
fvocab : str, optional
|
||
|
|
File path to the vocabulary.Word counts are read from `fvocab` filename, if set
|
||
|
|
(this is the file generated by `-save-vocab` flag of the original C tool).
|
||
|
|
binary : bool, optional
|
||
|
|
If True, indicates whether the data is in binary word2vec format.
|
||
|
|
encoding : str, optional
|
||
|
|
If you trained the C model using non-utf8 encoding for words, specify that encoding in `encoding`.
|
||
|
|
unicode_errors : str, optional
|
||
|
|
default 'strict', is a string suitable to be passed as the `errors`
|
||
|
|
argument to the unicode() (Python 2.x) or str() (Python 3.x) function. If your source
|
||
|
|
file may include word tokens truncated in the middle of a multibyte unicode character
|
||
|
|
(as is common from the original word2vec.c tool), 'ignore' or 'replace' may help.
|
||
|
|
limit : int, optional
|
||
|
|
Sets a maximum number of word-vectors to read from the file. The default,
|
||
|
|
None, means read all.
|
||
|
|
datatype : type, optional
|
||
|
|
(Experimental) Can coerce dimensions to a non-default float type (such as `np.float16`) to save memory.
|
||
|
|
Such types may result in much slower bulk operations or incompatibility with optimized routines.)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
:class:`~gensim.models.keyedvectors.Word2VecKeyedVectors`
|
||
|
|
Loaded model.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# from gensim.models.word2vec import load_word2vec_format
|
||
|
|
return _load_word2vec_format(
|
||
|
|
cls, fname, fvocab=fvocab, binary=binary, encoding=encoding, unicode_errors=unicode_errors,
|
||
|
|
limit=limit, datatype=datatype)
|
||
|
|
|
||
|
|
def get_keras_embedding(self, train_embeddings=False):
|
||
|
|
"""Get a Keras 'Embedding' layer with weights set as the Word2Vec model's learned word embeddings.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
train_embeddings : bool
|
||
|
|
If False, the weights are frozen and stopped from being updated.
|
||
|
|
If True, the weights can/will be further trained/updated.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
`keras.layers.Embedding`
|
||
|
|
Embedding layer.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
ImportError
|
||
|
|
If `Keras <https://pypi.org/project/Keras/>`_ not installed.
|
||
|
|
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
Current method work only if `Keras <https://pypi.org/project/Keras/>`_ installed.
|
||
|
|
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
from keras.layers import Embedding
|
||
|
|
except ImportError:
|
||
|
|
raise ImportError("Please install Keras to use this function")
|
||
|
|
weights = self.vectors
|
||
|
|
|
||
|
|
# set `trainable` as `False` to use the pretrained word embedding
|
||
|
|
# No extra mem usage here as `Embedding` layer doesn't create any new matrix for weights
|
||
|
|
layer = Embedding(
|
||
|
|
input_dim=weights.shape[0], output_dim=weights.shape[1],
|
||
|
|
weights=[weights], trainable=train_embeddings
|
||
|
|
)
|
||
|
|
return layer
|
||
|
|
|
||
|
|
|
||
|
|
KeyedVectors = Word2VecKeyedVectors # alias for backward compatibility
|
||
|
|
|
||
|
|
|
||
|
|
class Doc2VecKeyedVectors(BaseKeyedVectors):
|
||
|
|
|
||
|
|
def __init__(self, vector_size, mapfile_path):
|
||
|
|
super(Doc2VecKeyedVectors, self).__init__(vector_size=vector_size)
|
||
|
|
self.doctags = {} # string -> Doctag (only filled if necessary)
|
||
|
|
self.max_rawint = -1 # highest rawint-indexed doctag
|
||
|
|
self.offset2doctag = [] # int offset-past-(max_rawint+1) -> String (only filled if necessary)
|
||
|
|
self.count = 0
|
||
|
|
self.vectors_docs = []
|
||
|
|
self.mapfile_path = mapfile_path
|
||
|
|
self.vector_size = vector_size
|
||
|
|
self.vectors_docs_norm = None
|
||
|
|
|
||
|
|
@property
|
||
|
|
def index2entity(self):
|
||
|
|
return self.offset2doctag
|
||
|
|
|
||
|
|
@index2entity.setter
|
||
|
|
def index2entity(self, value):
|
||
|
|
self.offset2doctag = value
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use docvecs.vectors_docs instead")
|
||
|
|
def doctag_syn0(self):
|
||
|
|
return self.vectors_docs
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use docvecs.vectors_docs_norm instead")
|
||
|
|
def doctag_syn0norm(self):
|
||
|
|
return self.vectors_docs_norm
|
||
|
|
|
||
|
|
def __getitem__(self, index):
|
||
|
|
"""Get vector representation of `index`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
index : {str, list of str}
|
||
|
|
Doctag or sequence of doctags.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
Vector representation for `index` (1D if `index` is string, otherwise - 2D).
|
||
|
|
|
||
|
|
"""
|
||
|
|
if index in self:
|
||
|
|
if isinstance(index, string_types + integer_types + (integer,)):
|
||
|
|
return self.vectors_docs[self._int_index(index, self.doctags, self.max_rawint)]
|
||
|
|
return vstack([self[i] for i in index])
|
||
|
|
raise KeyError("tag '%s' not seen in training corpus/invalid" % index)
|
||
|
|
|
||
|
|
def __contains__(self, index):
|
||
|
|
if isinstance(index, integer_types + (integer,)):
|
||
|
|
return index < self.count
|
||
|
|
else:
|
||
|
|
return index in self.doctags
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.count
|
||
|
|
|
||
|
|
def save(self, *args, **kwargs):
|
||
|
|
"""Save object.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : str
|
||
|
|
Path to the output file.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:meth:`~gensim.models.keyedvectors.Doc2VecKeyedVectors.load`
|
||
|
|
Load object.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# don't bother storing the cached normalized vectors
|
||
|
|
kwargs['ignore'] = kwargs.get('ignore', ['vectors_docs_norm'])
|
||
|
|
super(Doc2VecKeyedVectors, self).save(*args, **kwargs)
|
||
|
|
|
||
|
|
def init_sims(self, replace=False):
|
||
|
|
"""Precompute L2-normalized vectors.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
replace : bool, optional
|
||
|
|
If True - forget the original vectors and only keep the normalized ones = saves lots of memory!
|
||
|
|
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
You **cannot continue training** after doing a replace.
|
||
|
|
The model becomes effectively read-only: you can call
|
||
|
|
:meth:`~gensim.models.keyedvectors.Doc2VecKeyedVectors.most_similar`,
|
||
|
|
:meth:`~gensim.models.keyedvectors.Doc2VecKeyedVectors.similarity`, etc., but not train and infer_vector.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if getattr(self, 'vectors_docs_norm', None) is None or replace:
|
||
|
|
logger.info("precomputing L2-norms of doc weight vectors")
|
||
|
|
if replace:
|
||
|
|
for i in xrange(self.vectors_docs.shape[0]):
|
||
|
|
self.vectors_docs[i, :] /= sqrt((self.vectors_docs[i, :] ** 2).sum(-1))
|
||
|
|
self.vectors_docs_norm = self.vectors_docs
|
||
|
|
else:
|
||
|
|
if self.mapfile_path:
|
||
|
|
self.vectors_docs_norm = np_memmap(
|
||
|
|
self.mapfile_path + '.vectors_docs_norm', dtype=REAL,
|
||
|
|
mode='w+', shape=self.vectors_docs.shape)
|
||
|
|
else:
|
||
|
|
self.vectors_docs_norm = empty(self.vectors_docs.shape, dtype=REAL)
|
||
|
|
np_divide(
|
||
|
|
self.vectors_docs, sqrt((self.vectors_docs ** 2).sum(-1))[..., newaxis], self.vectors_docs_norm)
|
||
|
|
|
||
|
|
def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip_end=None, indexer=None):
|
||
|
|
"""Find the top-N most similar docvecs from the training set.
|
||
|
|
Positive docvecs contribute positively towards the similarity, negative docvecs negatively.
|
||
|
|
|
||
|
|
This method computes cosine similarity between a simple mean of the projection
|
||
|
|
weight vectors of the given docs. Docs may be specified as vectors, integer indexes
|
||
|
|
of trained docvecs, or if the documents were originally presented with string tags,
|
||
|
|
by the corresponding tags.
|
||
|
|
|
||
|
|
TODO: Accept vectors of out-of-training-set docs, as if from inference.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
positive : list of {str, int}, optional
|
||
|
|
List of doctags/indexes that contribute positively.
|
||
|
|
negative : list of {str, int}, optional
|
||
|
|
List of doctags/indexes that contribute negatively.
|
||
|
|
topn : int, optional
|
||
|
|
Number of top-N similar docvecs to return.
|
||
|
|
clip_start : int
|
||
|
|
Start clipping index.
|
||
|
|
clip_end : int
|
||
|
|
End clipping index.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
list of ({str, int}, float)
|
||
|
|
Sequence of (doctag/index, similarity).
|
||
|
|
|
||
|
|
"""
|
||
|
|
if positive is None:
|
||
|
|
positive = []
|
||
|
|
if negative is None:
|
||
|
|
negative = []
|
||
|
|
|
||
|
|
self.init_sims()
|
||
|
|
clip_end = clip_end or len(self.vectors_docs_norm)
|
||
|
|
|
||
|
|
if isinstance(positive, string_types + integer_types + (integer,)) and not negative:
|
||
|
|
# allow calls like most_similar('dog'), as a shorthand for most_similar(['dog'])
|
||
|
|
positive = [positive]
|
||
|
|
|
||
|
|
# add weights for each doc, if not already present; default to 1.0 for positive and -1.0 for negative docs
|
||
|
|
positive = [
|
||
|
|
(doc, 1.0) if isinstance(doc, string_types + integer_types + (ndarray, integer))
|
||
|
|
else doc for doc in positive
|
||
|
|
]
|
||
|
|
negative = [
|
||
|
|
(doc, -1.0) if isinstance(doc, string_types + integer_types + (ndarray, integer))
|
||
|
|
else doc for doc in negative
|
||
|
|
]
|
||
|
|
|
||
|
|
# compute the weighted average of all docs
|
||
|
|
all_docs, mean = set(), []
|
||
|
|
for doc, weight in positive + negative:
|
||
|
|
if isinstance(doc, ndarray):
|
||
|
|
mean.append(weight * doc)
|
||
|
|
elif doc in self.doctags or doc < self.count:
|
||
|
|
mean.append(weight * self.vectors_docs_norm[self._int_index(doc, self.doctags, self.max_rawint)])
|
||
|
|
all_docs.add(self._int_index(doc, self.doctags, self.max_rawint))
|
||
|
|
else:
|
||
|
|
raise KeyError("doc '%s' not in trained set" % doc)
|
||
|
|
if not mean:
|
||
|
|
raise ValueError("cannot compute similarity with no input")
|
||
|
|
mean = matutils.unitvec(array(mean).mean(axis=0)).astype(REAL)
|
||
|
|
|
||
|
|
if indexer is not None:
|
||
|
|
return indexer.most_similar(mean, topn)
|
||
|
|
|
||
|
|
dists = dot(self.vectors_docs_norm[clip_start:clip_end], mean)
|
||
|
|
if not topn:
|
||
|
|
return dists
|
||
|
|
best = matutils.argsort(dists, topn=topn + len(all_docs), reverse=True)
|
||
|
|
# ignore (don't return) docs from the input
|
||
|
|
result = [
|
||
|
|
(self._index_to_doctag(sim + clip_start, self.offset2doctag, self.max_rawint), float(dists[sim]))
|
||
|
|
for sim in best
|
||
|
|
if (sim + clip_start) not in all_docs
|
||
|
|
]
|
||
|
|
return result[:topn]
|
||
|
|
|
||
|
|
def doesnt_match(self, docs):
|
||
|
|
"""Which document from the given list doesn't go with the others from the training set?
|
||
|
|
|
||
|
|
TODO: Accept vectors of out-of-training-set docs, as if from inference.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
docs : list of {str, int}
|
||
|
|
Sequence of doctags/indexes.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
{str, int}
|
||
|
|
Doctag/index of the document farthest away from the mean of all the documents.
|
||
|
|
|
||
|
|
"""
|
||
|
|
self.init_sims()
|
||
|
|
|
||
|
|
docs = [doc for doc in docs if doc in self.doctags or 0 <= doc < self.count] # filter out unknowns
|
||
|
|
logger.debug("using docs %s", docs)
|
||
|
|
if not docs:
|
||
|
|
raise ValueError("cannot select a doc from an empty list")
|
||
|
|
vectors = vstack(
|
||
|
|
self.vectors_docs_norm[self._int_index(doc, self.doctags, self.max_rawint)] for doc in docs).astype(REAL)
|
||
|
|
mean = matutils.unitvec(vectors.mean(axis=0)).astype(REAL)
|
||
|
|
dists = dot(vectors, mean)
|
||
|
|
return sorted(zip(dists, docs))[0][1]
|
||
|
|
|
||
|
|
def similarity(self, d1, d2):
|
||
|
|
"""Compute cosine similarity between two docvecs from the training set.
|
||
|
|
|
||
|
|
TODO: Accept vectors of out-of-training-set docs, as if from inference.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
d1 : {int, str}
|
||
|
|
Doctag/index of document.
|
||
|
|
d2 : {int, str}
|
||
|
|
Doctag/index of document.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
float
|
||
|
|
The cosine similarity between the vectors of the two documents.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return dot(matutils.unitvec(self[d1]), matutils.unitvec(self[d2]))
|
||
|
|
|
||
|
|
def n_similarity(self, ds1, ds2):
|
||
|
|
"""Compute cosine similarity between two sets of docvecs from the trained set.
|
||
|
|
|
||
|
|
TODO: Accept vectors of out-of-training-set docs, as if from inference.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
ds1 : list of {str, int}
|
||
|
|
Set of document as sequence of doctags/indexes.
|
||
|
|
ds2 : list of {str, int}
|
||
|
|
Set of document as sequence of doctags/indexes.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
float
|
||
|
|
The cosine similarity between the means of the documents in each of the two sets.
|
||
|
|
|
||
|
|
"""
|
||
|
|
v1 = [self[doc] for doc in ds1]
|
||
|
|
v2 = [self[doc] for doc in ds2]
|
||
|
|
return dot(matutils.unitvec(array(v1).mean(axis=0)), matutils.unitvec(array(v2).mean(axis=0)))
|
||
|
|
|
||
|
|
def distance(self, d1, d2):
|
||
|
|
"""
|
||
|
|
Compute cosine distance between two documents.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return 1 - self.similarity(d1, d2)
|
||
|
|
|
||
|
|
# required by base keyed vectors class
|
||
|
|
def distances(self, d1, other_docs=()):
|
||
|
|
"""Compute cosine distances from given `d1` to all documents in `other_docs`.
|
||
|
|
|
||
|
|
TODO: Accept vectors of out-of-training-set docs, as if from inference.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
d1 : {str, numpy.ndarray}
|
||
|
|
Doctag/index of document.
|
||
|
|
other_docs : iterable of {str, int}
|
||
|
|
Sequence of doctags/indexes.
|
||
|
|
If None or empty, distance of `d1` from all doctags in vocab is computed (including itself).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.array
|
||
|
|
Array containing distances to all documents in `other_docs` from input `d1`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
input_vector = self[d1]
|
||
|
|
if not other_docs:
|
||
|
|
other_vectors = self.vectors_docs
|
||
|
|
else:
|
||
|
|
other_vectors = self[other_docs]
|
||
|
|
return 1 - WordEmbeddingsKeyedVectors.cosine_similarities(input_vector, other_vectors)
|
||
|
|
|
||
|
|
def similarity_unseen_docs(self, model, doc_words1, doc_words2, alpha=0.1, min_alpha=0.0001, steps=5):
|
||
|
|
"""Compute cosine similarity between two post-bulk out of training documents.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
model : :class:`~gensim.models.doc2vec.Doc2Vec`
|
||
|
|
An instance of a trained `Doc2Vec` model.
|
||
|
|
doc_words1 : list of str
|
||
|
|
Input document.
|
||
|
|
doc_words2 : list of str
|
||
|
|
Input document.
|
||
|
|
alpha : float, optional
|
||
|
|
The initial learning rate.
|
||
|
|
min_alpha : float, optional
|
||
|
|
Learning rate will linearly drop to `min_alpha` as training progresses.
|
||
|
|
steps : int, optional
|
||
|
|
Number of epoch to train the new document.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
float
|
||
|
|
The cosine similarity between `doc_words1` and `doc_words2`.
|
||
|
|
|
||
|
|
"""
|
||
|
|
d1 = model.infer_vector(doc_words=doc_words1, alpha=alpha, min_alpha=min_alpha, steps=steps)
|
||
|
|
d2 = model.infer_vector(doc_words=doc_words2, alpha=alpha, min_alpha=min_alpha, steps=steps)
|
||
|
|
return dot(matutils.unitvec(d1), matutils.unitvec(d2))
|
||
|
|
|
||
|
|
def save_word2vec_format(self, fname, prefix='*dt_', fvocab=None,
|
||
|
|
total_vec=None, binary=False, write_first_line=True):
|
||
|
|
"""Store the input-hidden weight matrix in the same format used by the original
|
||
|
|
C word2vec-tool, for compatibility.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : str
|
||
|
|
The file path used to save the vectors in.
|
||
|
|
prefix : str, optional
|
||
|
|
Uniquely identifies doctags from word vocab, and avoids collision
|
||
|
|
in case of repeated string in doctag and word vocab.
|
||
|
|
fvocab : str, optional
|
||
|
|
UNUSED.
|
||
|
|
total_vec : int, optional
|
||
|
|
Explicitly specify total no. of vectors
|
||
|
|
(in case word vectors are appended with document vectors afterwards)
|
||
|
|
binary : bool, optional
|
||
|
|
If True, the data wil be saved in binary word2vec format, else it will be saved in plain text.
|
||
|
|
write_first_line : bool, optional
|
||
|
|
Whether to print the first line in the file. Useful when saving doc-vectors after word-vectors.
|
||
|
|
|
||
|
|
"""
|
||
|
|
total_vec = total_vec or len(self)
|
||
|
|
with utils.smart_open(fname, 'ab') as fout:
|
||
|
|
if write_first_line:
|
||
|
|
logger.info("storing %sx%s projection weights into %s", total_vec, self.vectors_docs.shape[1], fname)
|
||
|
|
fout.write(utils.to_utf8("%s %s\n" % (total_vec, self.vectors_docs.shape[1])))
|
||
|
|
# store as in input order
|
||
|
|
for i in range(len(self)):
|
||
|
|
doctag = u"%s%s" % (prefix, self._index_to_doctag(i, self.offset2doctag, self.max_rawint))
|
||
|
|
row = self.vectors_docs[i]
|
||
|
|
if binary:
|
||
|
|
fout.write(utils.to_utf8(doctag) + b" " + row.tostring())
|
||
|
|
else:
|
||
|
|
fout.write(utils.to_utf8("%s %s\n" % (doctag, ' '.join("%f" % val for val in row))))
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _int_index(index, doctags, max_rawint):
|
||
|
|
"""Get int index for either string or int index."""
|
||
|
|
if isinstance(index, integer_types + (integer,)):
|
||
|
|
return index
|
||
|
|
else:
|
||
|
|
return max_rawint + 1 + doctags[index].offset
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _index_to_doctag(i_index, offset2doctag, max_rawint):
|
||
|
|
"""Get string key for given `i_index`, if available. Otherwise return raw int doctag (same int)."""
|
||
|
|
candidate_offset = i_index - max_rawint - 1
|
||
|
|
if 0 <= candidate_offset < len(offset2doctag):
|
||
|
|
return offset2doctag[candidate_offset]
|
||
|
|
else:
|
||
|
|
return i_index
|
||
|
|
|
||
|
|
# for backward compatibility
|
||
|
|
def index_to_doctag(self, i_index):
|
||
|
|
"""Get string key for given `i_index`, if available. Otherwise return raw int doctag (same int)."""
|
||
|
|
candidate_offset = i_index - self.max_rawint - 1
|
||
|
|
if 0 <= candidate_offset < len(self.offset2doctag):
|
||
|
|
return self.offset2doctag[candidate_offset]
|
||
|
|
else:
|
||
|
|
return i_index
|
||
|
|
|
||
|
|
# for backward compatibility
|
||
|
|
def int_index(self, index, doctags, max_rawint):
|
||
|
|
"""Get int index for either string or int index"""
|
||
|
|
if isinstance(index, integer_types + (integer,)):
|
||
|
|
return index
|
||
|
|
else:
|
||
|
|
return max_rawint + 1 + doctags[index].offset
|
||
|
|
|
||
|
|
|
||
|
|
class FastTextKeyedVectors(WordEmbeddingsKeyedVectors):
|
||
|
|
"""Vectors and vocab for :class:`~gensim.models.fasttext.FastText`."""
|
||
|
|
def __init__(self, vector_size, min_n, max_n):
|
||
|
|
super(FastTextKeyedVectors, self).__init__(vector_size=vector_size)
|
||
|
|
self.vectors_vocab = None
|
||
|
|
self.vectors_vocab_norm = None
|
||
|
|
self.vectors_ngrams = None
|
||
|
|
self.vectors_ngrams_norm = None
|
||
|
|
self.buckets_word = None
|
||
|
|
self.hash2index = {}
|
||
|
|
self.min_n = min_n
|
||
|
|
self.max_n = max_n
|
||
|
|
self.num_ngram_vectors = 0
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors_vocab instead")
|
||
|
|
def syn0_vocab(self):
|
||
|
|
return self.vectors_vocab
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors_vocab_norm instead")
|
||
|
|
def syn0_vocab_norm(self):
|
||
|
|
return self.vectors_vocab_norm
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors_ngrams instead")
|
||
|
|
def syn0_ngrams(self):
|
||
|
|
return self.vectors_ngrams
|
||
|
|
|
||
|
|
@property
|
||
|
|
@deprecated("Attribute will be removed in 4.0.0, use self.wv.vectors_ngrams_norm instead")
|
||
|
|
def syn0_ngrams_norm(self):
|
||
|
|
return self.vectors_ngrams_norm
|
||
|
|
|
||
|
|
def __contains__(self, word):
|
||
|
|
"""Check if `word` or any character ngrams in `word` are present in the vocabulary.
|
||
|
|
A vector for the word is guaranteed to exist if current method returns True.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
word : str
|
||
|
|
Input word.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
bool
|
||
|
|
True if `word` or any character ngrams in `word` are present in the vocabulary, False otherwise.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if word in self.vocab:
|
||
|
|
return True
|
||
|
|
else:
|
||
|
|
char_ngrams = _compute_ngrams(word, self.min_n, self.max_n)
|
||
|
|
return any(_ft_hash(ng) % self.bucket in self.hash2index for ng in char_ngrams)
|
||
|
|
|
||
|
|
def save(self, *args, **kwargs):
|
||
|
|
"""Save object.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : str
|
||
|
|
Path to the output file.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:meth:`~gensim.models.keyedvectors.FastTextKeyedVectors.load`
|
||
|
|
Load object.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# don't bother storing the cached normalized vectors
|
||
|
|
kwargs['ignore'] = kwargs.get(
|
||
|
|
'ignore', ['vectors_norm', 'vectors_vocab_norm', 'vectors_ngrams_norm', 'buckets_word'])
|
||
|
|
super(FastTextKeyedVectors, self).save(*args, **kwargs)
|
||
|
|
|
||
|
|
def word_vec(self, word, use_norm=False):
|
||
|
|
"""Get `word` representations in vector space, as a 1D numpy array.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
word : str
|
||
|
|
Input word
|
||
|
|
use_norm : bool, optional
|
||
|
|
If True - resulting vector will be L2-normalized (unit euclidean length).
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
numpy.ndarray
|
||
|
|
Vector representation of `word`.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
KeyError
|
||
|
|
If word and all ngrams not in vocabulary.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if word in self.vocab:
|
||
|
|
return super(FastTextKeyedVectors, self).word_vec(word, use_norm)
|
||
|
|
else:
|
||
|
|
# from gensim.models.fasttext import compute_ngrams
|
||
|
|
word_vec = np.zeros(self.vectors_ngrams.shape[1], dtype=np.float32)
|
||
|
|
ngrams = _compute_ngrams(word, self.min_n, self.max_n)
|
||
|
|
if use_norm:
|
||
|
|
ngram_weights = self.vectors_ngrams_norm
|
||
|
|
else:
|
||
|
|
ngram_weights = self.vectors_ngrams
|
||
|
|
ngrams_found = 0
|
||
|
|
for ngram in ngrams:
|
||
|
|
ngram_hash = _ft_hash(ngram) % self.bucket
|
||
|
|
if ngram_hash in self.hash2index:
|
||
|
|
word_vec += ngram_weights[self.hash2index[ngram_hash]]
|
||
|
|
ngrams_found += 1
|
||
|
|
if word_vec.any():
|
||
|
|
return word_vec / max(1, ngrams_found)
|
||
|
|
else: # No ngrams of the word are present in self.ngrams
|
||
|
|
raise KeyError('all ngrams for word %s absent from model' % word)
|
||
|
|
|
||
|
|
def init_sims(self, replace=False):
|
||
|
|
"""Precompute L2-normalized vectors.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
replace : bool, optional
|
||
|
|
If True - forget the original vectors and only keep the normalized ones = saves lots of memory!
|
||
|
|
|
||
|
|
Warnings
|
||
|
|
--------
|
||
|
|
You **cannot continue training** after doing a replace.
|
||
|
|
The model becomes effectively read-only: you can call
|
||
|
|
:meth:`~gensim.models.keyedvectors.FastTextKeyedVectors.most_similar`,
|
||
|
|
:meth:`~gensim.models.keyedvectors.FastTextKeyedVectors.similarity`, etc., but not train.
|
||
|
|
|
||
|
|
"""
|
||
|
|
super(FastTextKeyedVectors, self).init_sims(replace)
|
||
|
|
if getattr(self, 'vectors_ngrams_norm', None) is None or replace:
|
||
|
|
logger.info("precomputing L2-norms of ngram weight vectors")
|
||
|
|
if replace:
|
||
|
|
for i in range(self.vectors_ngrams.shape[0]):
|
||
|
|
self.vectors_ngrams[i, :] /= sqrt((self.vectors_ngrams[i, :] ** 2).sum(-1))
|
||
|
|
self.vectors_ngrams_norm = self.vectors_ngrams
|
||
|
|
else:
|
||
|
|
self.vectors_ngrams_norm = \
|
||
|
|
(self.vectors_ngrams / sqrt((self.vectors_ngrams ** 2).sum(-1))[..., newaxis]).astype(REAL)
|
||
|
|
|
||
|
|
def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None):
|
||
|
|
"""Store the input-hidden weight matrix in the same format used by the original
|
||
|
|
C word2vec-tool, for compatibility.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
fname : str
|
||
|
|
The file path used to save the vectors in
|
||
|
|
fvocab : str, optional
|
||
|
|
Optional file path used to save the vocabulary
|
||
|
|
binary : bool, optional
|
||
|
|
If True, the data wil be saved in binary word2vec format, else it will be saved in plain text.
|
||
|
|
total_vec : int, optional
|
||
|
|
Optional parameter to explicitly specify total no. of vectors
|
||
|
|
(in case word vectors are appended with document vectors afterwards).
|
||
|
|
|
||
|
|
"""
|
||
|
|
# from gensim.models.word2vec import save_word2vec_format
|
||
|
|
_save_word2vec_format(
|
||
|
|
fname, self.vocab, self.vectors, fvocab=fvocab, binary=binary, total_vec=total_vec)
|