#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2013 Radim Rehurek # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html """ Warnings -------- .. deprecated:: 3.3.0 Use :mod:`gensim.models.doc2vec` instead. Deep learning via the distributed memory and distributed bag of words models from [1]_, using either hierarchical softmax or negative sampling [2]_ [3]_. See [#tutorial]_ **Make sure you have a C compiler before installing gensim, to use optimized (compiled) doc2vec training** (70x speedup [blog]_). Initialize a model with e.g.:: >>> model = Doc2Vec(documents, size=100, window=8, min_count=5, workers=4) Persist a model to disk with:: >>> model.save(fname) >>> model = Doc2Vec.load(fname) # you can continue training with the loaded model! If you're finished training a model (=no more updates, only querying), you can do >>> model.delete_temporary_training_data(keep_doctags_vectors=True, keep_inference=True): to trim unneeded model memory = use (much) less RAM. .. [1] Quoc Le and Tomas Mikolov. Distributed Representations of Sentences and Documents. http://arxiv.org/pdf/1405.4053v2.pdf .. [2] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013. .. [3] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013. .. [blog] Optimizing word2vec in gensim, http://radimrehurek.com/2013/09/word2vec-in-python-part-two-optimizing/ .. [#tutorial] Doc2vec in gensim tutorial, https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-lee.ipynb """ import logging import os try: from queue import Queue except ImportError: from Queue import Queue # noqa:F401 from collections import namedtuple, defaultdict from timeit import default_timer from numpy import zeros, sum as np_sum, add as np_add, concatenate, \ repeat as np_repeat, array, float32 as REAL, empty, ones, memmap as np_memmap, \ sqrt, newaxis, ndarray, dot, vstack, dtype, divide as np_divide, integer from gensim import utils from gensim.utils import call_on_class_only, deprecated from gensim.models.deprecated.word2vec import Word2Vec, train_cbow_pair, train_sg_pair, train_batch_sg,\ MAX_WORDS_IN_BATCH from gensim.models.deprecated.keyedvectors import KeyedVectors from gensim.models.doc2vec import Doc2Vec as NewDoc2Vec from gensim.models.deprecated.old_saveload import SaveLoad from gensim import matutils # utility fnc for pickling, common scipy operations etc from six.moves import xrange, zip from six import string_types, integer_types logger = logging.getLogger(__name__) def load_old_doc2vec(*args, **kwargs): old_model = Doc2Vec.load(*args, **kwargs) params = { 'dm_mean': old_model.__dict__.get('dm_mean', None), 'dm': old_model.dm, 'dbow_words': old_model.dbow_words, 'dm_concat': old_model.dm_concat, 'dm_tag_count': old_model.dm_tag_count, 'docvecs_mapfile': old_model.__dict__.get('docvecs_mapfile', None), 'comment': old_model.__dict__.get('comment', None), 'size': old_model.vector_size, 'alpha': old_model.alpha, 'window': old_model.window, 'min_count': old_model.min_count, 'max_vocab_size': old_model.__dict__.get('max_vocab_size', None), 'sample': old_model.sample, 'seed': old_model.seed, 'workers': old_model.workers, 'min_alpha': old_model.min_alpha, 'hs': old_model.hs, 'negative': old_model.negative, 'cbow_mean': old_model.cbow_mean, 'hashfxn': old_model.hashfxn, 'iter': old_model.iter, 'sorted_vocab': old_model.__dict__.get('sorted_vocab', 1), 'batch_words': old_model.__dict__.get('batch_words', MAX_WORDS_IN_BATCH), 'compute_loss': old_model.__dict__.get('compute_loss', None) } new_model = NewDoc2Vec(**params) # set word2vec trainables attributes new_model.wv.vectors = old_model.wv.syn0 if hasattr(old_model.wv, 'syn0norm'): new_model.docvecs.vectors_norm = old_model.wv.syn0norm if hasattr(old_model, 'syn1'): new_model.trainables.syn1 = old_model.syn1 if hasattr(old_model, 'syn1neg'): new_model.trainables.syn1neg = old_model.syn1neg if hasattr(old_model, 'syn0_lockf'): new_model.trainables.vectors_lockf = old_model.syn0_lockf # set doc2vec trainables attributes new_model.docvecs.vectors_docs = old_model.docvecs.doctag_syn0 if hasattr(old_model.docvecs, 'doctag_syn0norm'): new_model.docvecs.vectors_docs_norm = old_model.docvecs.doctag_syn0norm if hasattr(old_model.docvecs, 'doctag_syn0_lockf'): new_model.trainables.vectors_docs_lockf = old_model.docvecs.doctag_syn0_lockf if hasattr(old_model.docvecs, 'mapfile_path'): new_model.docvecs.mapfile_path = old_model.docvecs.mapfile_path # set word2vec vocabulary attributes new_model.wv.vocab = old_model.wv.vocab new_model.wv.index2word = old_model.wv.index2word new_model.vocabulary.cum_table = old_model.cum_table # set doc2vec vocabulary attributes new_model.docvecs.doctags = old_model.docvecs.doctags new_model.docvecs.count = old_model.docvecs.count if hasattr(old_model.docvecs, 'max_rawint'): # `doc2vec` models before `0.12.3` do not have these 2 attributes new_model.docvecs.max_rawint = old_model.docvecs.__dict__.get('max_rawint') new_model.docvecs.offset2doctag = old_model.docvecs.__dict__.get('offset2doctag') else: # Doc2Vec models before Gensim version 0.12.3 did not have `max_rawint` and `offset2doctag` as they did not # mixing of string and int tags. This implies the new attribute `offset2doctag` equals the old `index2doctag` # (which was only filled if the documents had string tags). # This also implies that the new attribute, `max_rawint`(highest rawint-indexed doctag) would either be equal # to the initial value -1, in case only string tags are used or would be equal to `count` if only int indexing # was used. new_model.docvecs.max_rawint = -1 if old_model.docvecs.index2doctag else old_model.docvecs.count - 1 new_model.docvecs.offset2doctag = old_model.docvecs.index2doctag new_model.train_count = old_model.__dict__.get('train_count', None) new_model.corpus_count = old_model.__dict__.get('corpus_count', None) new_model.running_training_loss = old_model.__dict__.get('running_training_loss', 0) new_model.total_train_time = old_model.__dict__.get('total_train_time', None) new_model.min_alpha_yet_reached = old_model.__dict__.get('min_alpha_yet_reached', old_model.alpha) new_model.model_trimmed_post_training = old_model.__dict__.get('model_trimmed_post_training', None) return new_model def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, train_words=False, learn_doctags=True, learn_words=True, learn_hidden=True, word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None): """ Update distributed bag of words model ("PV-DBOW") by training on a single document. Called internally from `Doc2Vec.train()` and `Doc2Vec.infer_vector()`. The document is provided as `doc_words`, a list of word tokens which are looked up in the model's vocab dictionary, and `doctag_indexes`, which provide indexes into the doctag_vectors array. If `train_words` is True, simultaneously train word-to-word (not just doc-to-word) examples, exactly as per Word2Vec skip-gram training. (Without this option, word vectors are neither consulted nor updated during DBOW doc vector training.) Any of `learn_doctags', `learn_words`, and `learn_hidden` may be set False to prevent learning-updates to those respective model weights, as if using the (partially-)frozen model to infer other compatible vectors. This is the non-optimized, Python version. If you have cython installed, gensim will use the optimized version from doc2vec_inner instead. """ if doctag_vectors is None: doctag_vectors = model.docvecs.doctag_syn0 if doctag_locks is None: doctag_locks = model.docvecs.doctag_syn0_lockf if train_words and learn_words: train_batch_sg(model, [doc_words], alpha, work) for doctag_index in doctag_indexes: for word in doc_words: train_sg_pair( model, word, doctag_index, alpha, learn_vectors=learn_doctags, learn_hidden=learn_hidden, context_vectors=doctag_vectors, context_locks=doctag_locks ) return len(doc_words) def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=None, learn_doctags=True, learn_words=True, learn_hidden=True, word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None): """ Update distributed memory model ("PV-DM") by training on a single document. Called internally from `Doc2Vec.train()` and `Doc2Vec.infer_vector()`. This method implements the DM model with a projection (input) layer that is either the sum or mean of the context vectors, depending on the model's `dm_mean` configuration field. See `train_document_dm_concat()` for the DM model with a concatenated input layer. The document is provided as `doc_words`, a list of word tokens which are looked up in the model's vocab dictionary, and `doctag_indexes`, which provide indexes into the doctag_vectors array. Any of `learn_doctags', `learn_words`, and `learn_hidden` may be set False to prevent learning-updates to those respective model weights, as if using the (partially-)frozen model to infer other compatible vectors. This is the non-optimized, Python version. If you have a C compiler, gensim will use the optimized version from doc2vec_inner instead. """ if word_vectors is None: word_vectors = model.wv.syn0 if word_locks is None: word_locks = model.syn0_lockf if doctag_vectors is None: doctag_vectors = model.docvecs.doctag_syn0 if doctag_locks is None: doctag_locks = model.docvecs.doctag_syn0_lockf word_vocabs = [model.wv.vocab[w] for w in doc_words if w in model.wv.vocab and model.wv.vocab[w].sample_int > model.random.rand() * 2**32] for pos, word in enumerate(word_vocabs): reduced_window = model.random.randint(model.window) # `b` in the original doc2vec code start = max(0, pos - model.window + reduced_window) window_pos = enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start) word2_indexes = [word2.index for pos2, word2 in window_pos if pos2 != pos] l1 = np_sum(word_vectors[word2_indexes], axis=0) + np_sum(doctag_vectors[doctag_indexes], axis=0) count = len(word2_indexes) + len(doctag_indexes) if model.cbow_mean and count > 1: l1 /= count neu1e = train_cbow_pair(model, word, word2_indexes, l1, alpha, learn_vectors=False, learn_hidden=learn_hidden) if not model.cbow_mean and count > 1: neu1e /= count if learn_doctags: for i in doctag_indexes: doctag_vectors[i] += neu1e * doctag_locks[i] if learn_words: for i in word2_indexes: word_vectors[i] += neu1e * word_locks[i] return len(word_vocabs) def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, neu1=None, learn_doctags=True, learn_words=True, learn_hidden=True, word_vectors=None, word_locks=None, doctag_vectors=None, doctag_locks=None): """ Update distributed memory model ("PV-DM") by training on a single document, using a concatenation of the context window word vectors (rather than a sum or average). Called internally from `Doc2Vec.train()` and `Doc2Vec.infer_vector()`. The document is provided as `doc_words`, a list of word tokens which are looked up in the model's vocab dictionary, and `doctag_indexes`, which provide indexes into the doctag_vectors array. Any of `learn_doctags', `learn_words`, and `learn_hidden` may be set False to prevent learning-updates to those respective model weights, as if using the (partially-)frozen model to infer other compatible vectors. This is the non-optimized, Python version. If you have a C compiler, gensim will use the optimized version from doc2vec_inner instead. """ if word_vectors is None: word_vectors = model.wv.syn0 if word_locks is None: word_locks = model.syn0_lockf if doctag_vectors is None: doctag_vectors = model.docvecs.doctag_syn0 if doctag_locks is None: doctag_locks = model.docvecs.doctag_syn0_lockf word_vocabs = [model.wv.vocab[w] for w in doc_words if w in model.wv.vocab and model.wv.vocab[w].sample_int > model.random.rand() * 2**32] doctag_len = len(doctag_indexes) if doctag_len != model.dm_tag_count: return 0 # skip doc without expected number of doctag(s) (TODO: warn/pad?) null_word = model.wv.vocab['\0'] pre_pad_count = model.window post_pad_count = model.window padded_document_indexes = ( (pre_pad_count * [null_word.index]) # pre-padding + [word.index for word in word_vocabs if word is not None] # elide out-of-Vocabulary words + (post_pad_count * [null_word.index]) # post-padding ) for pos in range(pre_pad_count, len(padded_document_indexes) - post_pad_count): word_context_indexes = ( padded_document_indexes[(pos - pre_pad_count): pos] # preceding words + padded_document_indexes[(pos + 1):(pos + 1 + post_pad_count)] # following words ) predict_word = model.wv.vocab[model.wv.index2word[padded_document_indexes[pos]]] # numpy advanced-indexing copies; concatenate, flatten to 1d l1 = concatenate((doctag_vectors[doctag_indexes], word_vectors[word_context_indexes])).ravel() neu1e = train_cbow_pair(model, predict_word, None, l1, alpha, learn_hidden=learn_hidden, learn_vectors=False) # filter by locks and shape for addition to source vectors e_locks = concatenate((doctag_locks[doctag_indexes], word_locks[word_context_indexes])) neu1e_r = (neu1e.reshape(-1, model.vector_size) * np_repeat(e_locks, model.vector_size).reshape(-1, model.vector_size)) if learn_doctags: np_add.at(doctag_vectors, doctag_indexes, neu1e_r[:doctag_len]) if learn_words: np_add.at(word_vectors, word_context_indexes, neu1e_r[doctag_len:]) return len(padded_document_indexes) - pre_pad_count - post_pad_count class TaggedDocument(namedtuple('TaggedDocument', 'words tags')): """ A single document, made up of `words` (a list of unicode string tokens) and `tags` (a list of tokens). Tags may be one or more unicode string tokens, but typical practice (which will also be most memory-efficient) is for the tags list to include a unique integer id as the only tag. Replaces "sentence as a list of words" from Word2Vec. """ def __str__(self): return '%s(%s, %s)' % (self.__class__.__name__, self.words, self.tags) # for compatibility @deprecated("Class will be removed in 4.0.0, use TaggedDocument instead") class LabeledSentence(TaggedDocument): pass class DocvecsArray(SaveLoad): """ Default storage of doc vectors during/after training, in a numpy array. As the 'docvecs' property of a Doc2Vec model, allows access and comparison of document vectors. >>> docvec = d2v_model.docvecs[99] >>> docvec = d2v_model.docvecs['SENT_99'] # if string tag used in training >>> sims = d2v_model.docvecs.most_similar(99) >>> sims = d2v_model.docvecs.most_similar('SENT_99') >>> sims = d2v_model.docvecs.most_similar(docvec) If only plain int tags are presented during training, the dict (of string tag -> index) and list (of index -> string tag) stay empty, saving memory. Supplying a mapfile_path (as by initializing a Doc2Vec model with a 'docvecs_mapfile' value) will use a pair of memory-mapped files as the array backing for doctag_syn0/doctag_syn0_lockf values. The Doc2Vec model automatically uses this class, but a future alternative implementation, based on another persistence mechanism like LMDB, LevelDB, or SQLite, should also be possible. """ def __init__(self, mapfile_path=None): 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.mapfile_path = mapfile_path def note_doctag(self, key, document_no, document_length): """Note a document tag during initial corpus scan, for structure sizing.""" if isinstance(key, integer_types + (integer,)): self.max_rawint = max(self.max_rawint, key) else: if key in self.doctags: self.doctags[key] = self.doctags[key].repeat(document_length) else: self.doctags[key] = Doctag(len(self.offset2doctag), document_length, 1) self.offset2doctag.append(key) self.count = self.max_rawint + 1 + len(self.offset2doctag) def indexed_doctags(self, doctag_tokens): """Return indexes and backing-arrays used in training examples.""" return ([self._int_index(index) for index in doctag_tokens if index in self], self.doctag_syn0, self.doctag_syn0_lockf, doctag_tokens) def trained_item(self, indexed_tuple): """Persist any changes made to the given indexes (matching tuple previously returned by indexed_doctags()); a no-op for this implementation""" pass def _int_index(self, index): """Return int index for either string or int index""" if isinstance(index, integer_types + (integer,)): return index else: return self.max_rawint + 1 + self.doctags[index].offset @deprecated("Method will be removed in 4.0.0, use self.index_to_doctag instead") def _key_index(self, i_index, missing=None): """Return string index for given int index, if available""" return self.index_to_doctag(i_index) def index_to_doctag(self, i_index): """Return 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 def __getitem__(self, index): """ Accept a single key (int or string tag) or list of keys as input. If a single string or int, return designated tag's vector representation, as a 1D numpy array. If a list, return designated tags' vector representations as a 2D numpy array: #tags x #vector_size. """ if isinstance(index, string_types + integer_types + (integer,)): return self.doctag_syn0[self._int_index(index)] return vstack([self[i] for i in index]) def __len__(self): return self.count def __contains__(self, index): if isinstance(index, integer_types + (integer,)): return index < self.count else: return index in self.doctags def save(self, *args, **kwargs): # don't bother storing the cached normalized vectors kwargs['ignore'] = kwargs.get('ignore', ['syn0norm']) super(DocvecsArray, self).save(*args, **kwargs) def borrow_from(self, other_docvecs): self.count = other_docvecs.count self.doctags = other_docvecs.doctags self.offset2doctag = other_docvecs.offset2doctag def clear_sims(self): self.doctag_syn0norm = None def estimated_lookup_memory(self): """Estimated memory for tag lookup; 0 if using pure int tags.""" return 60 * len(self.offset2doctag) + 140 * len(self.doctags) def reset_weights(self, model): length = max(len(self.doctags), self.count) if self.mapfile_path: self.doctag_syn0 = np_memmap( self.mapfile_path + '.doctag_syn0', dtype=REAL, mode='w+', shape=(length, model.vector_size) ) self.doctag_syn0_lockf = np_memmap( self.mapfile_path + '.doctag_syn0_lockf', dtype=REAL, mode='w+', shape=(length,) ) self.doctag_syn0_lockf.fill(1.0) else: self.doctag_syn0 = empty((length, model.vector_size), dtype=REAL) self.doctag_syn0_lockf = ones((length,), dtype=REAL) # zeros suppress learning for i in xrange(length): # construct deterministic seed from index AND model seed seed = "%d %s" % (model.seed, self.index_to_doctag(i)) self.doctag_syn0[i] = model.seeded_vector(seed) def init_sims(self, 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 or inference** after doing a replace. The model becomes effectively read-only = you can call `most_similar`, `similarity` etc., but not `train` or `infer_vector`. """ if getattr(self, 'doctag_syn0norm', None) is None or replace: logger.info("precomputing L2-norms of doc weight vectors") if replace: for i in xrange(self.doctag_syn0.shape[0]): self.doctag_syn0[i, :] /= sqrt((self.doctag_syn0[i, :] ** 2).sum(-1)) self.doctag_syn0norm = self.doctag_syn0 else: if self.mapfile_path: self.doctag_syn0norm = np_memmap( self.mapfile_path + '.doctag_syn0norm', dtype=REAL, mode='w+', shape=self.doctag_syn0.shape) else: self.doctag_syn0norm = empty(self.doctag_syn0.shape, dtype=REAL) np_divide(self.doctag_syn0, sqrt((self.doctag_syn0 ** 2).sum(-1))[..., newaxis], self.doctag_syn0norm) 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 known from training. Positive docs contribute positively towards the similarity, negative docs 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. The 'clip_start' and 'clip_end' allow limiting results to a particular contiguous range of the underlying doctag_syn0norm vectors. (This may be useful if the ordering there was chosen to be significant, such as more popular tag IDs in lower indexes.) """ if positive is None: positive = [] if negative is None: negative = [] self.init_sims() clip_end = clip_end or len(self.doctag_syn0norm) 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.doctag_syn0norm[self._int_index(doc)]) all_docs.add(self._int_index(doc)) 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.doctag_syn0norm[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), float(dists[sim])) for sim in best if (sim + clip_start) not in all_docs ] return result[:topn] def doesnt_match(self, docs): """ Which doc from the given list doesn't go with the others? (TODO: Accept vectors of out-of-training-set docs, as if from inference.) """ 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.doctag_syn0norm[self._int_index(doc)] 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 in the trained set, specified by int index or string tag. (TODO: Accept vectors of out-of-training-set docs, as if from inference.) """ 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, specified by int index or string tag. (TODO: Accept vectors of out-of-training-set docs, as if from inference.) """ 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 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. Document should be a list of (word) tokens. """ 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)) class Doctag(namedtuple('Doctag', 'offset, word_count, doc_count')): """A string document tag discovered during the initial vocabulary scan. (The document-vector equivalent of a Vocab object.) Will not be used if all presented document tags are ints. The offset is only the true index into the doctags_syn0/doctags_syn0_lockf if-and-only-if no raw-int tags were used. If any raw-int tags were used, string Doctag vectors begin at index (max_rawint + 1), so the true index is (rawint_index + 1 + offset). See also DocvecsArray.index_to_doctag(). """ __slots__ = () def repeat(self, word_count): return self._replace(word_count=self.word_count + word_count, doc_count=self.doc_count + 1) class Doc2Vec(Word2Vec): """Class for training, using and evaluating neural networks described in http://arxiv.org/pdf/1405.4053v2.pdf""" def __init__(self, documents=None, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1, docvecs=None, docvecs_mapfile=None, comment=None, trim_rule=None, **kwargs): """ Initialize the model from an iterable of `documents`. Each document is a TaggedDocument object that will be used for training. The `documents` iterable can be simply a list of TaggedDocument elements, but for larger corpora, consider an iterable that streams the documents directly from disk/network. If you don't supply `documents`, the model is left uninitialized -- use if you plan to initialize it in some other way. `dm` defines the training algorithm. By default (`dm=1`), 'distributed memory' (PV-DM) is used. Otherwise, `distributed bag of words` (PV-DBOW) is employed. `size` is the dimensionality of the feature vectors. `window` is the maximum distance between the predicted word and context words used for prediction within a document. `alpha` is the initial learning rate (will linearly drop to `min_alpha` as training progresses). `seed` = for the random number generator. Note that for a fully deterministically-reproducible run, you must also limit the model to a single worker thread, to eliminate ordering jitter from OS thread scheduling. (In Python 3, reproducibility between interpreter launches also requires use of the PYTHONHASHSEED environment variable to control hash randomization.) `min_count` = ignore all words with total frequency lower than this. `max_vocab_size` = limit RAM during vocabulary building; if there are more unique words than this, then prune the infrequent ones. Every 10 million word types need about 1GB of RAM. Set to `None` for no limit (default). `sample` = threshold for configuring which higher-frequency words are randomly downsampled; default is 1e-3, values of 1e-5 (or lower) may also be useful, set to 0.0 to disable downsampling. `workers` = use this many worker threads to train the model (=faster training with multicore machines). `iter` = number of iterations (epochs) over the corpus. The default inherited from Word2Vec is 5, but values of 10 or 20 are common in published 'Paragraph Vector' experiments. `hs` = if 1, hierarchical softmax will be used for model training. If set to 0 (default), and `negative` is non-zero, negative sampling will be used. `negative` = if > 0, negative sampling will be used, the int for negative specifies how many "noise words" should be drawn (usually between 5-20). Default is 5. If set to 0, no negative samping is used. `dm_mean` = if 0 (default), use the sum of the context word vectors. If 1, use the mean. Only applies when dm is used in non-concatenative mode. `dm_concat` = if 1, use concatenation of context vectors rather than sum/average; default is 0 (off). Note concatenation results in a much-larger model, as the input is no longer the size of one (sampled or arithmetically combined) word vector, but the size of the tag(s) and all words in the context strung together. `dm_tag_count` = expected constant number of document tags per document, when using dm_concat mode; default is 1. `dbow_words` if set to 1 trains word-vectors (in skip-gram fashion) simultaneous with DBOW doc-vector training; default is 0 (faster training of doc-vectors only). `trim_rule` = vocabulary trimming rule, specifies whether certain words should remain in the vocabulary, be trimmed away, or handled using the default (discard if word count < min_count). Can be None (min_count will be used), or a callable that accepts parameters (word, count, min_count) and returns either util.RULE_DISCARD, util.RULE_KEEP or util.RULE_DEFAULT. Note: The rule, if given, is only used prune vocabulary during build_vocab() and is not stored as part of the model. """ if 'sentences' in kwargs: raise DeprecationWarning( "Parameter 'sentences' was renamed to 'documents', and will be removed in 4.0.0, " "use 'documents' instead." ) super(Doc2Vec, self).__init__( sg=(1 + dm) % 2, null_word=dm_concat, **kwargs) self.load = call_on_class_only if dm_mean is not None: self.cbow_mean = dm_mean self.dbow_words = dbow_words self.dm_concat = dm_concat self.dm_tag_count = dm_tag_count if self.dm and self.dm_concat: self.layer1_size = (self.dm_tag_count + (2 * self.window)) * self.vector_size self.docvecs = docvecs or DocvecsArray(docvecs_mapfile) self.comment = comment if documents is not None: self.build_vocab(documents, trim_rule=trim_rule) self.train(documents, total_examples=self.corpus_count, epochs=self.iter) @property def dm(self): return not self.sg # opposite of SG @property def dbow(self): return self.sg # same as SG def clear_sims(self): super(Doc2Vec, self).clear_sims() self.docvecs.clear_sims() def reset_weights(self): if self.dm and self.dm_concat: # expand l1 size to match concatenated tags+words length self.layer1_size = (self.dm_tag_count + (2 * self.window)) * self.vector_size logger.info("using concatenative %d-dimensional layer1", self.layer1_size) super(Doc2Vec, self).reset_weights() self.docvecs.reset_weights(self) def reset_from(self, other_model): """Reuse shareable structures from other_model.""" self.docvecs.borrow_from(other_model.docvecs) super(Doc2Vec, self).reset_from(other_model) def scan_vocab(self, documents, progress_per=10000, trim_rule=None, update=False): logger.info("collecting all words and their counts") document_no = -1 total_words = 0 min_reduce = 1 interval_start = default_timer() - 0.00001 # guard against next sample being identical interval_count = 0 checked_string_types = 0 vocab = defaultdict(int) for document_no, document in enumerate(documents): if not checked_string_types: if isinstance(document.words, string_types): logger.warning( "Each 'words' should be a list of words (usually unicode strings). " "First 'words' here is instead plain %s.", type(document.words) ) checked_string_types += 1 if document_no % progress_per == 0: interval_rate = (total_words - interval_count) / (default_timer() - interval_start) logger.info( "PROGRESS: at example #%i, processed %i words (%i/s), %i word types, %i tags", document_no, total_words, interval_rate, len(vocab), len(self.docvecs) ) interval_start = default_timer() interval_count = total_words document_length = len(document.words) for tag in document.tags: self.docvecs.note_doctag(tag, document_no, document_length) for word in document.words: vocab[word] += 1 total_words += len(document.words) if self.max_vocab_size and len(vocab) > self.max_vocab_size: utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule) min_reduce += 1 logger.info( "collected %i word types and %i unique tags from a corpus of %i examples and %i words", len(vocab), len(self.docvecs), document_no + 1, total_words ) self.corpus_count = document_no + 1 self.raw_vocab = vocab def _do_train_job(self, job, alpha, inits): work, neu1 = inits tally = 0 for doc in job: indexed_doctags = self.docvecs.indexed_doctags(doc.tags) doctag_indexes, doctag_vectors, doctag_locks, ignored = indexed_doctags if self.sg: tally += train_document_dbow( self, doc.words, doctag_indexes, alpha, work, train_words=self.dbow_words, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks ) elif self.dm_concat: tally += train_document_dm_concat( self, doc.words, doctag_indexes, alpha, work, neu1, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks ) else: tally += train_document_dm( self, doc.words, doctag_indexes, alpha, work, neu1, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks ) self.docvecs.trained_item(indexed_doctags) return tally, self._raw_word_count(job) def _raw_word_count(self, job): """Return the number of words in a given job.""" return sum(len(sentence.words) for sentence in job) def infer_vector(self, doc_words, alpha=0.1, min_alpha=0.0001, steps=5): """ Infer a vector for given post-bulk training document. Document should be a list of (word) tokens. """ doctag_vectors = empty((1, self.vector_size), dtype=REAL) doctag_vectors[0] = self.seeded_vector(' '.join(doc_words)) doctag_locks = ones(1, dtype=REAL) doctag_indexes = [0] work = zeros(self.layer1_size, dtype=REAL) if not self.sg: neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL) for i in range(steps): if self.sg: train_document_dbow( self, doc_words, doctag_indexes, alpha, work, learn_words=False, learn_hidden=False, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks ) elif self.dm_concat: train_document_dm_concat( self, doc_words, doctag_indexes, alpha, work, neu1, learn_words=False, learn_hidden=False, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks ) else: train_document_dm( self, doc_words, doctag_indexes, alpha, work, neu1, learn_words=False, learn_hidden=False, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks ) alpha = ((alpha - min_alpha) / (steps - i)) + min_alpha return doctag_vectors[0] def estimate_memory(self, vocab_size=None, report=None): """Estimate required memory for a model using current settings.""" report = report or {} report['doctag_lookup'] = self.docvecs.estimated_lookup_memory() report['doctag_syn0'] = self.docvecs.count * self.vector_size * dtype(REAL).itemsize return super(Doc2Vec, self).estimate_memory(vocab_size, report=report) def __str__(self): """Abbreviated name reflecting major configuration paramaters.""" segments = [] if self.comment: segments.append('"%s"' % self.comment) if self.sg: if self.dbow_words: segments.append('dbow+w') # also training words else: segments.append('dbow') # PV-DBOW (skip-gram-style) else: # PV-DM... if self.dm_concat: segments.append('dm/c') # ...with concatenative context layer else: if self.cbow_mean: segments.append('dm/m') else: segments.append('dm/s') segments.append('d%d' % self.vector_size) # dimensions if self.negative: segments.append('n%d' % self.negative) # negative samples if self.hs: segments.append('hs') if not self.sg or (self.sg and self.dbow_words): segments.append('w%d' % self.window) # window size, when relevant if self.min_count > 1: segments.append('mc%d' % self.min_count) if self.sample > 0: segments.append('s%g' % self.sample) if self.workers > 1: segments.append('t%d' % self.workers) return '%s(%s)' % (self.__class__.__name__, ','.join(segments)) def delete_temporary_training_data(self, keep_doctags_vectors=True, keep_inference=True): """ Discard parameters that are used in training and score. Use if you're sure you're done training a model. Set `keep_doctags_vectors` to False if you don't want to save doctags vectors, in this case you can't to use docvecs's most_similar, similarity etc. methods. Set `keep_inference` to False if you don't want to store parameters that is used for infer_vector method """ if not keep_inference: self._minimize_model(False, False, False) if self.docvecs and hasattr(self.docvecs, 'doctag_syn0') and not keep_doctags_vectors: del self.docvecs.doctag_syn0 if self.docvecs and hasattr(self.docvecs, 'doctag_syn0_lockf'): del self.docvecs.doctag_syn0_lockf def save_word2vec_format(self, fname, doctag_vec=False, word_vec=True, prefix='*dt_', fvocab=None, binary=False): """ Store the input-hidden weight matrix. `fname` is the file used to save the vectors in `doctag_vec` is an optional boolean indicating whether to store document vectors `word_vec` is an optional boolean indicating whether to store word vectors (if both doctag_vec and word_vec are True, then both vectors are stored in the same file) `prefix` to uniquely identify doctags from word vocab, and avoid collision in case of repeated string in doctag and word vocab `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 = len(self.wv.vocab) + len(self.docvecs) # save word vectors if word_vec: if not doctag_vec: total_vec = len(self.wv.vocab) KeyedVectors.save_word2vec_format(self.wv, fname, fvocab, binary, total_vec) # save document vectors if doctag_vec: with utils.smart_open(fname, 'ab') as fout: if not word_vec: total_vec = len(self.docvecs) logger.info("storing %sx%s projection weights into %s", total_vec, self.vector_size, fname) fout.write(utils.to_utf8("%s %s\n" % (total_vec, self.vector_size))) # store as in input order for i in range(len(self.docvecs)): doctag = u"%s%s" % (prefix, self.docvecs.index_to_doctag(i)) row = self.docvecs.doctag_syn0[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)))) class TaggedBrownCorpus(object): """Iterate over documents from the Brown corpus (part of NLTK data), yielding each document out as a TaggedDocument object.""" def __init__(self, dirname): self.dirname = dirname def __iter__(self): for fname in os.listdir(self.dirname): fname = os.path.join(self.dirname, fname) if not os.path.isfile(fname): continue for item_no, line in enumerate(utils.smart_open(fname)): line = utils.to_unicode(line) # each file line is a single document in the Brown corpus # each token is WORD/POS_TAG token_tags = [t.split('/') for t in line.split() if len(t.split('/')) == 2] # ignore words with non-alphabetic tags like ",", "!" etc (punctuation, weird stuff) words = ["%s/%s" % (token.lower(), tag[:2]) for token, tag in token_tags if tag[:2].isalpha()] if not words: # don't bother sending out empty documents continue yield TaggedDocument(words, ['%s_SENT_%s' % (fname, item_no)]) class TaggedLineDocument(object): """Simple format: one document = one line = one TaggedDocument object. Words are expected to be already preprocessed and separated by whitespace, tags are constructed automatically from the document line number.""" def __init__(self, source): """ `source` can be either a string (filename) or a file object. Example:: documents = TaggedLineDocument('myfile.txt') Or for compressed files:: documents = TaggedLineDocument('compressed_text.txt.bz2') documents = TaggedLineDocument('compressed_text.txt.gz') """ self.source = source def __iter__(self): """Iterate through the lines in the source.""" try: # Assume it is a file-like object and try treating it as such # Things that don't have seek will trigger an exception self.source.seek(0) for item_no, line in enumerate(self.source): yield TaggedDocument(utils.to_unicode(line).split(), [item_no]) except AttributeError: # If it didn't work like a file, use it as a string filename with utils.smart_open(self.source) as fin: for item_no, line in enumerate(fin): yield TaggedDocument(utils.to_unicode(line).split(), [item_no])