#!/usr/bin/env python # -*- coding: utf-8 -*- # # Author: Shiva Manne # Copyright (C) 2018 RaRe Technologies s.r.o. # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html """This module implements word vectors and their similarity look-ups. Since trained word vectors are independent from the way they were trained (:class:`~gensim.models.word2vec.Word2Vec`, :class:`~gensim.models.fasttext.FastText`, :class:`~gensim.models.wrappers.wordrank.WordRank`, :class:`~gensim.models.wrappers.varembed.VarEmbed` etc), they can be represented by a standalone structure, as implemented in this module. The structure is called "KeyedVectors" and is essentially a mapping between *entities* and *vectors*. Each entity is identified by its string id, so this is a mapping between {str => 1D numpy array}. The entity typically corresponds to a word (so the mapping maps words to 1D vectors), but for some models, they key can also correspond to a document, a graph node etc. To generalize over different use-cases, this module calls the keys **entities**. Each entity is always represented by its string id, no matter whether the entity is a word, a document or a graph node. Why use KeyedVectors instead of a full model? ============================================= +---------------------------+--------------+------------+-------------------------------------------------------------+ | capability | KeyedVectors | full model | note | +---------------------------+--------------+------------+-------------------------------------------------------------+ | continue training vectors | ❌ | ✅ | You need the full model to train or update vectors. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | smaller objects | ✅ | ❌ | KeyedVectors are smaller and need less RAM, because they | | | | | don't need to store the model state that enables training. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | save/load from native | | | Vectors exported by the Facebook and Google tools | | fasttext/word2vec format | ✅ | ❌ | do not support further training, but you can still load | | | | | them into KeyedVectors. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | append new vectors | ✅ | ✅ | Add new entity-vector entries to the mapping dynamically. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | concurrency | ✅ | ✅ | Thread-safe, allows concurrent vector queries. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | shared RAM | ✅ | ✅ | Multiple processes can re-use the same data, keeping only | | | | | a single copy in RAM using | | | | | `mmap `_. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | fast load | ✅ | ✅ | Supports `mmap `_ | | | | | to load data from disk instantaneously. | +---------------------------+--------------+------------+-------------------------------------------------------------+ TL;DR: the main difference is that KeyedVectors do not support further training. On the other hand, by shedding the internal data structures necessary for training, KeyedVectors offer a smaller RAM footprint and a simpler interface. How to obtain word vectors? =========================== Train a full model, then access its `model.wv` property, which holds the standalone keyed vectors. For example, using the Word2Vec algorithm to train the vectors >>> from gensim.test.utils import common_texts >>> from gensim.models import Word2Vec >>> >>> model = Word2Vec(common_texts, size=100, window=5, min_count=1, workers=4) >>> word_vectors = model.wv Persist the word vectors to disk with >>> from gensim.test.utils import get_tmpfile >>> from gensim.models import KeyedVectors >>> >>> fname = get_tmpfile("vectors.kv") >>> word_vectors.save(fname) >>> word_vectors = KeyedVectors.load(fname, mmap='r') The vectors can also be instantiated from an existing file on disk in the original Google's word2vec C format as a KeyedVectors instance >>> from gensim.test.utils import datapath >>> >>> wv_from_text = KeyedVectors.load_word2vec_format(datapath('word2vec_pre_kv_c'), binary=False) # C text format >>> wv_from_bin = KeyedVectors.load_word2vec_format(datapath("euclidean_vectors.bin"), binary=True) # C binary format What can I do with word vectors? ================================ You can perform various syntactic/semantic NLP word tasks with the trained vectors. Some of them are already built-in >>> import gensim.downloader as api >>> >>> word_vectors = api.load("glove-wiki-gigaword-100") # load pre-trained word-vectors from gensim-data >>> >>> result = word_vectors.most_similar(positive=['woman', 'king'], negative=['man']) >>> print("{}: {:.4f}".format(*result[0])) queen: 0.7699 >>> >>> result = word_vectors.most_similar_cosmul(positive=['woman', 'king'], negative=['man']) >>> print("{}: {:.4f}".format(*result[0])) queen: 0.8965 >>> >>> print(word_vectors.doesnt_match("breakfast cereal dinner lunch".split())) cereal >>> >>> similarity = word_vectors.similarity('woman', 'man') >>> similarity > 0.8 True >>> >>> result = word_vectors.similar_by_word("cat") >>> print("{}: {:.4f}".format(*result[0])) dog: 0.8798 >>> >>> sentence_obama = 'Obama speaks to the media in Illinois'.lower().split() >>> sentence_president = 'The president greets the press in Chicago'.lower().split() >>> >>> similarity = word_vectors.wmdistance(sentence_obama, sentence_president) >>> print("{:.4f}".format(similarity)) 3.4893 >>> >>> distance = word_vectors.distance("media", "media") >>> print("{:.1f}".format(distance)) 0.0 >>> >>> sim = word_vectors.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant']) >>> print("{:.4f}".format(sim)) 0.7067 >>> >>> vector = word_vectors['computer'] # numpy vector of a word >>> vector.shape (100,) >>> >>> vector = word_vectors.wv.word_vec('office', use_norm=True) >>> vector.shape (100,) Correlation with human opinion on word similarity >>> from gensim.test.utils import datapath >>> >>> similarities = model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) And on word analogies >>> analogy_scores = model.wv.accuracy(datapath('questions-words.txt')) and so on. """ from __future__ import division # py3 "true division" import logging try: from queue import Queue, Empty except ImportError: from Queue import Queue, Empty # noqa:F401 # If pyemd C extension is available, import it. # If pyemd is attempted to be used, but isn't installed, ImportError will be raised in wmdistance try: from pyemd import emd PYEMD_EXT = True except ImportError: PYEMD_EXT = False from numpy import dot, float32 as REAL, empty, memmap as np_memmap, \ double, array, zeros, vstack, sqrt, newaxis, integer, \ ndarray, sum as np_sum, prod, argmax, divide as np_divide import numpy as np from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc from gensim.corpora.dictionary import Dictionary from six import string_types, integer_types from six.moves import xrange, zip from scipy import sparse, stats from gensim.utils import deprecated from gensim.models.utils_any2vec import _save_word2vec_format, _load_word2vec_format, _compute_ngrams, _ft_hash logger = logging.getLogger(__name__) class Vocab(object): """A single vocabulary item, used internally for collecting per-word frequency/sampling info, and for constructing binary trees (incl. both word leaves and inner nodes). """ def __init__(self, **kwargs): self.count = 0 self.__dict__.update(kwargs) def __lt__(self, other): # used for sorting in a priority queue return self.count < other.count def __str__(self): vals = ['%s:%r' % (key, self.__dict__[key]) for key in sorted(self.__dict__) if not key.startswith('_')] return "%s(%s)" % (self.__class__.__name__, ', '.join(vals)) class BaseKeyedVectors(utils.SaveLoad): """Abstract base class / interface for various types of word vectors.""" def __init__(self, vector_size): self.vectors = zeros((0, vector_size)) self.vocab = {} self.vector_size = vector_size self.index2entity = [] def save(self, fname_or_handle, **kwargs): super(BaseKeyedVectors, self).save(fname_or_handle, **kwargs) @classmethod def load(cls, fname_or_handle, **kwargs): return super(BaseKeyedVectors, cls).load(fname_or_handle, **kwargs) def similarity(self, entity1, entity2): """Compute cosine similarity between two entities, specified by their string id.""" raise NotImplementedError() def most_similar(self, **kwargs): """Find the top-N most similar entities. Possibly have `positive` and `negative` list of entities in `**kwargs`. """ return NotImplementedError() def distance(self, entity1, entity2): """Compute distance between vectors of two input entities, specified by their string id.""" raise NotImplementedError() def distances(self, entity1, other_entities=()): """Compute distances from a given entity (its string id) to all entities in `other_entity`. If `other_entities` is empty, return the distance between `entity1` and all entities in vocab. """ raise NotImplementedError() def get_vector(self, entity): """Get the entity's representations in vector space, as a 1D numpy array. Parameters ---------- entity : str Identifier of the entity to return the vector for. Returns ------- numpy.ndarray Vector for the specified entity. Raises ------ KeyError If the given entity identifier doesn't exist. """ if entity in self.vocab: result = self.vectors[self.vocab[entity].index] result.setflags(write=False) return result else: raise KeyError("'%s' not in vocabulary" % entity) def add(self, entities, weights, replace=False): """Append entities and theirs vectors in a manual way. If some entity is already in the vocabulary, the old vector is kept unless `replace` flag is True. Parameters ---------- entities : list of str Entities specified by string ids. weights: {list of numpy.ndarray, numpy.ndarray} List of 1D np.array vectors or a 2D np.array of vectors. replace: bool, optional Flag indicating whether to replace vectors for entities which already exist in the vocabulary, if True - replace vectors, otherwise - keep old vectors. """ if isinstance(entities, string_types): entities = [entities] weights = np.array(weights).reshape(1, -1) elif isinstance(weights, list): weights = np.array(weights) in_vocab_mask = np.zeros(len(entities), dtype=np.bool) for idx, entity in enumerate(entities): if entity in self.vocab: in_vocab_mask[idx] = True # add new entities to the vocab for idx in np.nonzero(~in_vocab_mask)[0]: entity = entities[idx] self.vocab[entity] = Vocab(index=len(self.vocab), count=1) self.index2entity.append(entity) # add vectors for new entities self.vectors = vstack((self.vectors, weights[~in_vocab_mask])) # change vectors for in_vocab entities if `replace` flag is specified if replace: in_vocab_idxs = [self.vocab[entities[idx]].index for idx in np.nonzero(in_vocab_mask)[0]] self.vectors[in_vocab_idxs] = weights[in_vocab_mask] def __setitem__(self, entities, weights): """Add entities and theirs vectors in a manual way. If some entity is already in the vocabulary, old vector is replaced with the new one. This method is alias for :meth:`~gensim.models.keyedvectors.BaseKeyedVectors.add` with `replace=True`. Parameters ---------- entities : {str, list of str} Entities specified by their string ids. weights: {list of numpy.ndarray, numpy.ndarray} List of 1D np.array vectors or 2D np.array of vectors. """ if not isinstance(entities, list): entities = [entities] weights = weights.reshape(1, -1) self.add(entities, weights, replace=True) def __getitem__(self, entities): """Get vector representation of `entities`. Parameters ---------- entities : {str, list of str} Input entity/entities. Returns ------- numpy.ndarray Vector representation for `entities` (1D if `entities` is string, otherwise - 2D). """ if isinstance(entities, string_types): # allow calls like trained_model['office'], as a shorthand for trained_model[['office']] return self.get_vector(entities) return vstack([self.get_vector(entity) for entity in entities]) def __contains__(self, entity): return entity in self.vocab def most_similar_to_given(self, entity1, entities_list): """Get the `entity` from `entities_list` most similar to `entity1`.""" return entities_list[argmax([self.similarity(entity1, entity) for entity in entities_list])] def closer_than(self, entity1, entity2): """Get all entities that are closer to `entity1` than `entity2` is to `entity1`.""" all_distances = self.distances(entity1) e1_index = self.vocab[entity1].index 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 `_. """ 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" `_ * `Ofir Pele and Michael Werman "Fast and robust earth mover's distances" `_ * `Matt Kusner et al. "From Word Embeddings To Document Distances" `_. 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 `_ 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 `_ 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" `_. 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 `_. 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) `_. 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 `_ not installed. Warnings -------- Current method work only if `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)