models.deprecated.keyedvectors – Store and query word vectors¶Warning
Deprecated since version 3.3.0: Use gensim.models.keyedvectors instead.
Word vector storage and similarity look-ups. Common code independent of the way the vectors are trained(Word2Vec, FastText, WordRank, VarEmbed etc)
The word vectors are considered read-only in this class.
Initialize the vectors by training e.g. Word2Vec:
>>> model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)
>>> word_vectors = model.wv
Persist the word vectors to disk with:
>>> word_vectors.save(fname)
>>> word_vectors = KeyedVectors.load(fname)
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.models.keyedvectors import KeyedVectors
>>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False) # C text format
>>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True) # C binary format
You can perform various syntactic/semantic NLP word tasks with the vectors. Some of them are already built-in:
>>> word_vectors.most_similar(positive=['woman', 'king'], negative=['man'])
[('queen', 0.50882536), ...]
>>> word_vectors.most_similar_cosmul(positive=['woman', 'king'], negative=['man'])
[('queen', 0.71382287), ...]
>>> word_vectors.doesnt_match("breakfast cereal dinner lunch".split())
'cereal'
>>> word_vectors.similarity('woman', 'man')
0.73723527
Correlation with human opinion on word similarity:
>>> word_vectors.evaluate_word_pairs(os.path.join(module_path, 'test_data','wordsim353.tsv'))
0.51, 0.62, 0.13
And on analogies:
>>> word_vectors.accuracy(os.path.join(module_path, 'test_data', 'questions-words.txt'))
and so on.
gensim.models.deprecated.keyedvectors.EuclideanKeyedVectors¶Bases: gensim.models.deprecated.keyedvectors.KeyedVectorsBase
Class to contain vectors and vocab for the Word2Vec training class and other w2v methods not directly involved in training such as most_similar()
accuracy(questions, restrict_vocab=30000, most_similar=<function EuclideanKeyedVectors.most_similar>, case_insensitive=True)¶Compute accuracy of the model. questions is a filename where lines are 4-tuples of words, split into sections by “: SECTION NAME” lines. See questions-words.txt in https://storage.googleapis.com/google-code-archive-source/v2/code.google.com/word2vec/source-archive.zip for an example.
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.
Use restrict_vocab to ignore all questions containing a word not in the first restrict_vocab words (default 30,000). This may be meaningful if you’ve sorted the vocabulary by descending frequency. In case case_insensitive is True, the first restrict_vocab words are taken first, and then case normalization is performed.
Use case_insensitive to convert all words in questions and vocab to their uppercase form before evaluating the accuracy (default True). Useful in case of case-mismatch between training tokens and question words. 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.
This method corresponds to the compute-accuracy script of the original C word2vec.
cosine_similarities(vector_1, vectors_all)¶Return cosine similarities between one vector and a set of other vectors.
vector_1 (numpy.array) – vector from which similarities are to be computed. expected shape (dim,)
vectors_all (numpy.array) – for each row in vectors_all, distance from vector_1 is computed. expected shape (num_vectors, dim)
Contains cosine distance between vector_1 and each row in vectors_all. shape (num_vectors,)
numpy.array
distance(w1, w2)¶Compute cosine distance between two words.
Example:
>>> trained_model.distance('woman', 'man')
0.34
>>> trained_model.distance('woman', 'woman')
0.0
distances(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.
word_or_vector (str or numpy.array) – Word or vector from which distances are to be computed.
other_words (iterable(str) or None) – 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).
Array containing distances to all words in other_words from input word_or_vector, in the same order as other_words.
numpy.array
Notes
Raises KeyError if either word_or_vector or any word in other_words is absent from vocab.
doesnt_match(words)¶Which word from the given list doesn’t go with the others?
Example:
>>> trained_model.doesnt_match("breakfast cereal dinner lunch".split())
'cereal'
evaluate_word_pairs(pairs, delimiter='\t', restrict_vocab=300000, case_insensitive=True, dummy4unknown=False)¶Compute correlation of the model with human similarity judgments. pairs is a filename of a dataset where lines are 3-tuples, each consisting of a word pair and a similarity value, separated by delimiter. An example dataset is included in Gensim (test/test_data/wordsim353.tsv). More datasets can be found at http://technion.ac.il/~ira.leviant/MultilingualVSMdata.html or https://www.cl.cam.ac.uk/~fh295/simlex.html.
The model is evaluated using Pearson correlation coefficient and Spearman rank-order correlation coefficient between the similarities from the dataset and the similarities produced by the model itself. The results are printed to log and returned as a triple (pearson, spearman, ratio of pairs with unknown words).
Use restrict_vocab to ignore all word pairs containing a word not in the first restrict_vocab words (default 300,000). This may be meaningful if you’ve sorted the vocabulary by descending frequency. If case_insensitive is True, the first restrict_vocab words are taken, and then case normalization is performed.
Use case_insensitive to convert all words in the pairs and vocab to their uppercase form before evaluating the model (default True). Useful when you expect case-mismatch between training tokens and words pairs in the dataset. If there are multiple case variants of a single word, the vector for the first occurrence (also the most frequent if vocabulary is sorted) is taken.
Use dummy4unknown=True to produce zero-valued similarities for pairs with out-of-vocabulary words. Otherwise (default False), these pairs are skipped entirely.
get_keras_embedding(train_embeddings=False)¶Return a Keras ‘Embedding’ layer with weights set as the Word2Vec model’s learned word embeddings
init_sims(replace=False)¶Precompute L2-normalized vectors.
If replace is set, forget the original vectors and only keep the normalized ones = saves lots of memory!
Note that you cannot continue training after doing a replace. The model becomes effectively read-only = you can call most_similar, similarity etc., but not train.
load(fname, mmap=None)¶Load an object previously saved using save() from a file.
fname (str) – Path to file that contains needed object.
mmap (str, optional) – Memory-map option. If the object was saved with large arrays stored separately, you can load these arrays via mmap (shared memory) using mmap=’r’. If the file being loaded is compressed (either ‘.gz’ or ‘.bz2’), then `mmap=None must be set.
See also
save()Save object to file.
Object loaded from fname.
object
AttributeError – When called on an object instance instead of class (this is a class method).
load_word2vec_format(fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', limit=None, datatype=<class 'numpy.float32'>)¶Load the input-hidden weight matrix from the original C word2vec-tool format.
Note that 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.
binary is a boolean indicating whether the data is in binary word2vec format. norm_only is a boolean indicating whether to only store normalised word2vec vectors in memory. Word counts are read from fvocab filename, if set (this is the file generated by -save-vocab flag of the original C tool).
If you trained the C model using non-utf8 encoding for words, specify that encoding in encoding.
unicode_errors, 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 sets a maximum number of word-vectors to read from the file. The default, None, means read all.
datatype (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.)
log_accuracy(section)¶log_evaluate_word_pairs(pearson, spearman, oov, pairs)¶most_similar(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.
If topn is False, most_similar returns the vector of similarity scores.
restrict_vocab is an 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.)
Example:
>>> trained_model.most_similar(positive=['woman', 'king'], negative=['man'])
[('queen', 0.50882536), ...]
most_similar_cosmul(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 in 4. 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 most_similar.)
Example:
>>> trained_model.most_similar_cosmul(positive=['baghdad', 'england'], negative=['london'])
[(u'iraq', 0.8488819003105164), ...]
Omer Levy and Yoav Goldberg. Linguistic Regularities in Sparse and Explicit Word Representations, 2014.
most_similar_to_given(w1, word_list)¶Return the word from word_list most similar to w1.
w1 (str): a word word_list (list): list of words containing a word most similar to w1
the word in word_list with the highest similarity to w1
KeyError: If w1 or any word in word_list is not in the vocabulary
Example:
>>> trained_model.most_similar_to_given('music', ['water', 'sound', 'backpack', 'mouse'])
'sound'
>>> trained_model.most_similar_to_given('snake', ['food', 'pencil', 'animal', 'phone'])
'animal'
n_similarity(ws1, ws2)¶Compute cosine similarity between two sets of words.
Example:
>>> trained_model.n_similarity(['sushi', 'shop'], ['japanese', 'restaurant'])
0.61540466561049689
>>> trained_model.n_similarity(['restaurant', 'japanese'], ['japanese', 'restaurant'])
1.0000000000000004
>>> trained_model.n_similarity(['sushi'], ['restaurant']) == trained_model.similarity('sushi', 'restaurant')
True
rank(w1, w2)¶Rank of the distance of w2 from w1, in relation to distances of all words from w1.
w1 (str) – Input word.
w2 (str) – Input word.
Rank of w2 from w1 in relation to all other nodes.
int
Examples
>>> model.rank('mammal.n.01', 'carnivore.n.01')
3
save(*args, **kwargs)¶Save the object to a file.
fname_or_handle (str or file-like) – Path to output file or already opened file-like object. If the object is a file handle, no special array handling will be performed, all attributes will be saved to the same file.
separately (list of str or None, optional) –
If None, automatically detect large numpy/scipy.sparse arrays in the object being stored, and store them into separate files. This prevent memory errors for large objects, and also allows memory-mapping the large arrays for efficient loading and sharing the large arrays in RAM between multiple processes.
If list of str: store these attributes into separate files. The automated size check is not performed in this case.
sep_limit (int, optional) – Don’t store arrays smaller than this separately. In bytes.
ignore (frozenset of str, optional) – Attributes that shouldn’t be stored at all.
pickle_protocol (int, optional) – Protocol number for pickle.
See also
load()Load object from file.
save_word2vec_format(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.
fname is the file used to save the vectors in fvocab is an optional file used to save the vocabulary binary is an optional boolean indicating whether the data is to be saved in binary word2vec format (default: False) total_vec is an optional parameter to explicitly specify total no. of vectors (in case word vectors are appended with document vectors afterwards)
similar_by_vector(vector, topn=10, restrict_vocab=None)¶Find the top-N most similar words by vector.
If topn is False, similar_by_vector returns the vector of similarity scores.
restrict_vocab is an 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.)
Example:
>>> trained_model.similar_by_vector([1,2])
[('survey', 0.9942699074745178), ...]
similar_by_word(word, topn=10, restrict_vocab=None)¶Find the top-N most similar words.
If topn is False, similar_by_word returns the vector of similarity scores.
restrict_vocab is an 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.)
Example:
>>> trained_model.similar_by_word('graph')
[('user', 0.9999163150787354), ...]
similarity(w1, w2)¶Compute cosine similarity between two words.
Example:
>>> trained_model.similarity('woman', 'man')
0.73723527
>>> trained_model.similarity('woman', 'woman')
1.0
wmdistance(document1, document2)¶Compute the Word Mover’s Distance between two documents. When using this code, please consider citing the following papers:
Note that if one of the documents have no words that exist in the Word2Vec vocab, float(‘inf’) (i.e. infinity) will be returned.
This method only works if pyemd is installed (can be installed via pip, but requires a C compiler).
Example:
>>> # Train word2vec model.
>>> model = Word2Vec(sentences)
>>> # Some sentences to test.
>>> sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()
>>> sentence_president = 'The president greets the press in Chicago'.lower().split()
>>> # Remove their stopwords.
>>> from nltk.corpus import stopwords
>>> stopwords = nltk.corpus.stopwords.words('english')
>>> sentence_obama = [w for w in sentence_obama if w not in stopwords]
>>> sentence_president = [w for w in sentence_president if w not in stopwords]
>>> # Compute WMD.
>>> distance = model.wmdistance(sentence_obama, sentence_president)
word_vec(word, use_norm=False)¶Accept a single word as input. Returns the word’s representations in vector space, as a 1D numpy array.
If use_norm is True, returns the normalized word vector.
Example:
>>> trained_model['office']
array([ -1.40128313e-02, ...])
words_closer_than(w1, w2)¶Returns all words that are closer to w1 than w2 is to w1.
w1 (str) – Input word.
w2 (str) – Input word.
List of words that are closer to w1 than w2 is to w1.
list (str)
Examples
>>> model.words_closer_than('carnivore.n.01', 'mammal.n.01')
['dog.n.01', 'canine.n.02']
wv¶gensim.models.deprecated.keyedvectors.KeyedVectors¶alias of gensim.models.deprecated.keyedvectors.EuclideanKeyedVectors
gensim.models.deprecated.keyedvectors.KeyedVectorsBase¶Bases: gensim.utils.SaveLoad
Base class to contain vectors and vocab for any set of vectors which are each associated with a key.
distance(w1, w2)¶Compute distance between vectors of two input words. To be implemented by child class.
distances(word_or_vector, other_words=())¶Compute 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. To be implemented by child class.
load(fname, mmap=None)¶Load an object previously saved using save() from a file.
fname (str) – Path to file that contains needed object.
mmap (str, optional) – Memory-map option. If the object was saved with large arrays stored separately, you can load these arrays via mmap (shared memory) using mmap=’r’. If the file being loaded is compressed (either ‘.gz’ or ‘.bz2’), then `mmap=None must be set.
See also
save()Save object to file.
Object loaded from fname.
object
AttributeError – When called on an object instance instead of class (this is a class method).
load_word2vec_format(fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', limit=None, datatype=<class 'numpy.float32'>)¶Load the input-hidden weight matrix from the original C word2vec-tool format.
Note that 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.
binary is a boolean indicating whether the data is in binary word2vec format. norm_only is a boolean indicating whether to only store normalised word2vec vectors in memory. Word counts are read from fvocab filename, if set (this is the file generated by -save-vocab flag of the original C tool).
If you trained the C model using non-utf8 encoding for words, specify that encoding in encoding.
unicode_errors, 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 sets a maximum number of word-vectors to read from the file. The default, None, means read all.
datatype (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.)
most_similar_to_given(w1, word_list)¶Return the word from word_list most similar to w1.
w1 (str): a word word_list (list): list of words containing a word most similar to w1
the word in word_list with the highest similarity to w1
KeyError: If w1 or any word in word_list is not in the vocabulary
Example:
>>> trained_model.most_similar_to_given('music', ['water', 'sound', 'backpack', 'mouse'])
'sound'
>>> trained_model.most_similar_to_given('snake', ['food', 'pencil', 'animal', 'phone'])
'animal'
rank(w1, w2)¶Rank of the distance of w2 from w1, in relation to distances of all words from w1.
w1 (str) – Input word.
w2 (str) – Input word.
Rank of w2 from w1 in relation to all other nodes.
int
Examples
>>> model.rank('mammal.n.01', 'carnivore.n.01')
3
save(fname_or_handle, separately=None, sep_limit=10485760, ignore=frozenset({}), pickle_protocol=2)¶Save the object to a file.
fname_or_handle (str or file-like) – Path to output file or already opened file-like object. If the object is a file handle, no special array handling will be performed, all attributes will be saved to the same file.
separately (list of str or None, optional) –
If None, automatically detect large numpy/scipy.sparse arrays in the object being stored, and store them into separate files. This prevent memory errors for large objects, and also allows memory-mapping the large arrays for efficient loading and sharing the large arrays in RAM between multiple processes.
If list of str: store these attributes into separate files. The automated size check is not performed in this case.
sep_limit (int, optional) – Don’t store arrays smaller than this separately. In bytes.
ignore (frozenset of str, optional) – Attributes that shouldn’t be stored at all.
pickle_protocol (int, optional) – Protocol number for pickle.
See also
load()Load object from file.
save_word2vec_format(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.
fname is the file used to save the vectors in fvocab is an optional file used to save the vocabulary binary is an optional boolean indicating whether the data is to be saved in binary word2vec format (default: False) total_vec is an optional parameter to explicitly specify total no. of vectors (in case word vectors are appended with document vectors afterwards)
similarity(w1, w2)¶Compute similarity between vectors of two input words. To be implemented by child class.
word_vec(word)¶Accept a single word as input. Returns the word’s representations in vector space, as a 1D numpy array.
Example:
>>> trained_model.word_vec('office')
array([ -1.40128313e-02, ...])
words_closer_than(w1, w2)¶Returns all words that are closer to w1 than w2 is to w1.
w1 (str) – Input word.
w2 (str) – Input word.
List of words that are closer to w1 than w2 is to w1.
list (str)
Examples
>>> model.words_closer_than('carnivore.n.01', 'mammal.n.01')
['dog.n.01', 'canine.n.02']
gensim.models.deprecated.keyedvectors.Vocab(**kwargs)¶Bases: 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).
