#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (C) 2011 Radim Rehurek # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html """ Automatically detect common phrases -- aka multi-word expressions, word n-gram collocations -- from a stream of sentences. Inspired by: * `Mikolov, et. al: "Distributed Representations of Words and Phrases and their Compositionality" `_ * `"Normalized (Pointwise) Mutual Information in Collocation Extraction" by Gerlof Bouma `_ Examples -------- .. sourcecode:: pycon >>> from gensim.test.utils import datapath >>> from gensim.models.word2vec import Text8Corpus >>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS >>> >>> # Create training corpus. Must be a sequence of sentences (e.g. an iterable or a generator). >>> sentences = Text8Corpus(datapath('testcorpus.txt')) >>> # Each sentence must be a list of string tokens: >>> first_sentence = next(iter(sentences)) >>> print(first_sentence[:10]) ['computer', 'human', 'interface', 'computer', 'response', 'survey', 'system', 'time', 'user', 'interface'] >>> >>> # Train a toy phrase model on our training corpus. >>> phrase_model = Phrases(sentences, min_count=1, threshold=1, connector_words=ENGLISH_CONNECTOR_WORDS) >>> >>> # Apply the trained phrases model to a new, unseen sentence. >>> new_sentence = ['trees', 'graph', 'minors'] >>> phrase_model[new_sentence] ['trees_graph', 'minors'] >>> # The toy model considered "trees graph" a single phrase => joined the two >>> # tokens into a single "phrase" token, using our selected `_` delimiter. >>> >>> # Apply the trained model to each sentence of a corpus, using the same [] syntax: >>> for sent in phrase_model[sentences]: ... pass >>> >>> # Update the model with two new sentences on the fly. >>> phrase_model.add_vocab([["hello", "world"], ["meow"]]) >>> >>> # Export the trained model = use less RAM, faster processing. Model updates no longer possible. >>> frozen_model = phrase_model.freeze() >>> # Apply the frozen model; same results as before: >>> frozen_model[new_sentence] ['trees_graph', 'minors'] >>> >>> # Save / load models. >>> frozen_model.save("/tmp/my_phrase_model.pkl") >>> model_reloaded = Phrases.load("/tmp/my_phrase_model.pkl") >>> model_reloaded[['trees', 'graph', 'minors']] # apply the reloaded model to a sentence ['trees_graph', 'minors'] """ import logging import itertools from math import log import pickle from inspect import getfullargspec as getargspec import time from gensim import utils, interfaces logger = logging.getLogger(__name__) NEGATIVE_INFINITY = float('-inf') # Words from this set are "ignored" during phrase detection: # 1) Phrases may not start nor end with these words. # 2) Phrases may include any number of these words inside. ENGLISH_CONNECTOR_WORDS = frozenset( " a an the " # articles; we never care about these in MWEs " for of with without at from to in on by " # prepositions; incomplete on purpose, to minimize FNs " and or " # conjunctions; incomplete on purpose, to minimize FNs .split() ) def original_scorer(worda_count, wordb_count, bigram_count, len_vocab, min_count, corpus_word_count): r"""Bigram scoring function, based on the original `Mikolov, et. al: "Distributed Representations of Words and Phrases and their Compositionality" `_. Parameters ---------- worda_count : int Number of occurrences for first word. wordb_count : int Number of occurrences for second word. bigram_count : int Number of co-occurrences for phrase "worda_wordb". len_vocab : int Size of vocabulary. min_count: int Minimum collocation count threshold. corpus_word_count : int Not used in this particular scoring technique. Returns ------- float Score for given phrase. Can be negative. Notes ----- Formula: :math:`\frac{(bigram\_count - min\_count) * len\_vocab }{ (worda\_count * wordb\_count)}`. """ denom = worda_count * wordb_count if denom == 0: return NEGATIVE_INFINITY return (bigram_count - min_count) / float(denom) * len_vocab def npmi_scorer(worda_count, wordb_count, bigram_count, len_vocab, min_count, corpus_word_count): r"""Calculation NPMI score based on `"Normalized (Pointwise) Mutual Information in Colocation Extraction" by Gerlof Bouma `_. Parameters ---------- worda_count : int Number of occurrences for first word. wordb_count : int Number of occurrences for second word. bigram_count : int Number of co-occurrences for phrase "worda_wordb". len_vocab : int Not used. min_count: int Ignore all bigrams with total collected count lower than this value. corpus_word_count : int Total number of words in the corpus. Returns ------- float If bigram_count >= min_count, return the collocation score, in the range -1 to 1. Otherwise return -inf. Notes ----- Formula: :math:`\frac{ln(prop(word_a, word_b) / (prop(word_a)*prop(word_b)))}{ -ln(prop(word_a, word_b)}`, where :math:`prob(word) = \frac{word\_count}{corpus\_word\_count}` """ if bigram_count >= min_count: corpus_word_count = float(corpus_word_count) pa = worda_count / corpus_word_count pb = wordb_count / corpus_word_count pab = bigram_count / corpus_word_count try: return log(pab / (pa * pb)) / -log(pab) except ValueError: # some of the counts were zero => never a phrase return NEGATIVE_INFINITY else: # Return -infinity to make sure that no phrases will be created # from bigrams less frequent than min_count. return NEGATIVE_INFINITY def _is_single(obj): """Check whether `obj` is a single document or an entire corpus. Parameters ---------- obj : object Return ------ (bool, object) 2-tuple ``(is_single_document, new_obj)`` tuple, where `new_obj` yields the same sequence as the original `obj`. Notes ----- `obj` is a single document if it is an iterable of strings. It is a corpus if it is an iterable of documents. """ obj_iter = iter(obj) temp_iter = obj_iter try: peek = next(obj_iter) obj_iter = itertools.chain([peek], obj_iter) except StopIteration: # An empty object is interpreted as a single document (not a corpus). return True, obj if isinstance(peek, str): # First item is a string => obj is a single document for sure. return True, obj_iter if temp_iter is obj: # An iterator / generator => interpret input as a corpus. return False, obj_iter # If the first item isn't a string, assume obj is an iterable corpus. return False, obj class _PhrasesTransformation(interfaces.TransformationABC): """ Abstract base class for :class:`~gensim.models.phrases.Phrases` and :class:`~gensim.models.phrases.FrozenPhrases`. """ def __init__(self, connector_words): self.connector_words = frozenset(connector_words) def score_candidate(self, word_a, word_b, in_between): """Score a single phrase candidate. Returns ------- (str, float) 2-tuple of ``(delimiter-joined phrase, phrase score)`` for a phrase, or ``(None, None)`` if not a phrase. """ raise NotImplementedError("ABC: override this method in child classes") def analyze_sentence(self, sentence): """Analyze a sentence, concatenating any detected phrases into a single token. Parameters ---------- sentence : iterable of str Token sequence representing the sentence to be analyzed. Yields ------ (str, {float, None}) Iterate through the input sentence tokens and yield 2-tuples of: - ``(concatenated_phrase_tokens, score)`` for token sequences that form a phrase. - ``(word, None)`` if the token is not a part of a phrase. """ start_token, in_between = None, [] for word in sentence: if word not in self.connector_words: # The current word is a normal token, not a connector word, which means it's a potential # beginning (or end) of a phrase. if start_token: # We're inside a potential phrase, of which this word is the end. phrase, score = self.score_candidate(start_token, word, in_between) if score is not None: # Phrase detected! yield phrase, score start_token, in_between = None, [] else: # Not a phrase after all. Dissolve the candidate's constituent tokens as individual words. yield start_token, None for w in in_between: yield w, None start_token, in_between = word, [] # new potential phrase starts here else: # Not inside a phrase yet; start a new phrase candidate here. start_token, in_between = word, [] else: # We're a connector word. if start_token: # We're inside a potential phrase: add the connector word and keep growing the phrase. in_between.append(word) else: # Not inside a phrase: emit the connector word and move on. yield word, None # Emit any non-phrase tokens at the end. if start_token: yield start_token, None for w in in_between: yield w, None def __getitem__(self, sentence): """Convert the input sequence of tokens ``sentence`` into a sequence of tokens where adjacent tokens are replaced by a single token if they form a bigram collocation. If `sentence` is an entire corpus (iterable of sentences rather than a single sentence), return an iterable that converts each of the corpus' sentences into phrases on the fly, one after another. Parameters ---------- sentence : {list of str, iterable of list of str} Input sentence or a stream of sentences. Return ------ {list of str, iterable of list of str} Sentence with phrase tokens joined by ``self.delimiter``, if input was a single sentence. A generator of such sentences if input was a corpus. s """ is_single, sentence = _is_single(sentence) if not is_single: # If the input is an entire corpus (rather than a single sentence), # return an iterable stream. return self._apply(sentence) return [token for token, _ in self.analyze_sentence(sentence)] def find_phrases(self, sentences): """Get all unique phrases (multi-word expressions) that appear in ``sentences``, and their scores. Parameters ---------- sentences : iterable of list of str Text corpus. Returns ------- dict(str, float) Unique phrases found in ``sentences``, mapped to their scores. Example ------- .. sourcecode:: pycon >>> from gensim.test.utils import datapath >>> from gensim.models.word2vec import Text8Corpus >>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS >>> >>> sentences = Text8Corpus(datapath('testcorpus.txt')) >>> phrases = Phrases(sentences, min_count=1, threshold=0.1, connector_words=ENGLISH_CONNECTOR_WORDS) >>> >>> for phrase, score in phrases.find_phrases(sentences).items(): ... print(phrase, score) """ result = {} for sentence in sentences: for phrase, score in self.analyze_sentence(sentence): if score is not None: result[phrase] = score return result @classmethod def load(cls, *args, **kwargs): """Load a previously saved :class:`~gensim.models.phrases.Phrases` / :class:`~gensim.models.phrases.FrozenPhrases` model. Handles backwards compatibility from older versions which did not support pluggable scoring functions. Parameters ---------- args : object See :class:`~gensim.utils.SaveLoad.load`. kwargs : object See :class:`~gensim.utils.SaveLoad.load`. """ model = super(_PhrasesTransformation, cls).load(*args, **kwargs) # Upgrade FrozenPhrases try: phrasegrams = getattr(model, "phrasegrams", {}) component, score = next(iter(phrasegrams.items())) if isinstance(score, tuple): # Value in phrasegrams used to be a tuple; keep only the 2nd tuple component = score. model.phrasegrams = { str(model.delimiter.join(key), encoding='utf8'): val[1] for key, val in phrasegrams.items() } elif isinstance(component, tuple): # 3.8 => 4.0: phrasegram keys are strings, not tuples with bytestrings model.phrasegrams = { str(model.delimiter.join(key), encoding='utf8'): val for key, val in phrasegrams.items() } except StopIteration: # no phrasegrams, nothing to upgrade pass # If no scoring parameter, use default scoring. if not hasattr(model, 'scoring'): logger.warning('older version of %s loaded without scoring function', cls.__name__) logger.warning('setting pluggable scoring method to original_scorer for compatibility') model.scoring = original_scorer # If there is a scoring parameter, and it's a text value, load the proper scoring function. if hasattr(model, 'scoring'): if isinstance(model.scoring, str): if model.scoring == 'default': logger.warning('older version of %s loaded with "default" scoring parameter', cls.__name__) logger.warning('setting scoring method to original_scorer for compatibility') model.scoring = original_scorer elif model.scoring == 'npmi': logger.warning('older version of %s loaded with "npmi" scoring parameter', cls.__name__) logger.warning('setting scoring method to npmi_scorer for compatibility') model.scoring = npmi_scorer else: raise ValueError(f'failed to load {cls.__name__} model, unknown scoring "{model.scoring}"') # common_terms didn't exist pre-3.?, and was renamed to connector in 4.0.0. if not hasattr(model, "connector_words"): if hasattr(model, "common_terms"): model.connector_words = model.common_terms del model.common_terms else: logger.warning('loaded older version of %s, setting connector_words to an empty set', cls.__name__) model.connector_words = frozenset() if not hasattr(model, 'corpus_word_count'): logger.warning('older version of %s loaded without corpus_word_count', cls.__name__) logger.warning('setting corpus_word_count to 0, do not use it in your scoring function') model.corpus_word_count = 0 # Before 4.0.0, we stored strings as UTF8 bytes internally, to save RAM. Since 4.0.0, we use strings. if getattr(model, 'vocab', None): word = next(iter(model.vocab)) # get a random key – any key will do if not isinstance(word, str): logger.info("old version of %s loaded, upgrading %i words in memory", cls.__name__, len(model.vocab)) logger.info("re-save the loaded model to avoid this upgrade in the future") vocab = {} for key, value in model.vocab.items(): # needs lots of extra RAM temporarily! vocab[str(key, encoding='utf8')] = value model.vocab = vocab if not isinstance(model.delimiter, str): model.delimiter = str(model.delimiter, encoding='utf8') return model class Phrases(_PhrasesTransformation): """Detect phrases based on collocation counts.""" def __init__( self, sentences=None, min_count=5, threshold=10.0, max_vocab_size=40000000, delimiter='_', progress_per=10000, scoring='default', connector_words=frozenset(), ): """ Parameters ---------- sentences : iterable of list of str, optional The `sentences` iterable can be simply a list, but for larger corpora, consider a generator that streams the sentences directly from disk/network, See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus` or :class:`~gensim.models.word2vec.LineSentence` for such examples. min_count : float, optional Ignore all words and bigrams with total collected count lower than this value. threshold : float, optional Represent a score threshold for forming the phrases (higher means fewer phrases). A phrase of words `a` followed by `b` is accepted if the score of the phrase is greater than threshold. Heavily depends on concrete scoring-function, see the `scoring` parameter. max_vocab_size : int, optional Maximum size (number of tokens) of the vocabulary. Used to control pruning of less common words, to keep memory under control. The default of 40M needs about 3.6GB of RAM. Increase/decrease `max_vocab_size` depending on how much available memory you have. delimiter : str, optional Glue character used to join collocation tokens. scoring : {'default', 'npmi', function}, optional Specify how potential phrases are scored. `scoring` can be set with either a string that refers to a built-in scoring function, or with a function with the expected parameter names. Two built-in scoring functions are available by setting `scoring` to a string: #. "default" - :func:`~gensim.models.phrases.original_scorer`. #. "npmi" - :func:`~gensim.models.phrases.npmi_scorer`. connector_words : set of str, optional Set of words that may be included within a phrase, without affecting its scoring. No phrase can start nor end with a connector word; a phrase may contain any number of connector words in the middle. **If your texts are in English, set** ``connector_words=phrases.ENGLISH_CONNECTOR_WORDS``. This will cause phrases to include common English articles, prepositions and conjuctions, such as `bank_of_america` or `eye_of_the_beholder`. For other languages or specific applications domains, use custom ``connector_words`` that make sense there: ``connector_words=frozenset("der die das".split())`` etc. Examples -------- .. sourcecode:: pycon >>> from gensim.test.utils import datapath >>> from gensim.models.word2vec import Text8Corpus >>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS >>> >>> # Load corpus and train a model. >>> sentences = Text8Corpus(datapath('testcorpus.txt')) >>> phrases = Phrases(sentences, min_count=1, threshold=1, connector_words=ENGLISH_CONNECTOR_WORDS) >>> >>> # Use the model to detect phrases in a new sentence. >>> sent = [u'trees', u'graph', u'minors'] >>> print(phrases[sent]) [u'trees_graph', u'minors'] >>> >>> # Or transform multiple sentences at once. >>> sents = [[u'trees', u'graph', u'minors'], [u'graph', u'minors']] >>> for phrase in phrases[sents]: ... print(phrase) [u'trees_graph', u'minors'] [u'graph_minors'] >>> >>> # Export a FrozenPhrases object that is more efficient but doesn't allow any more training. >>> frozen_phrases = phrases.freeze() >>> print(frozen_phrases[sent]) [u'trees_graph', u'minors'] Notes ----- The ``scoring="npmi"`` is more robust when dealing with common words that form part of common bigrams, and ranges from -1 to 1, but is slower to calculate than the default ``scoring="default"``. The default is the PMI-like scoring as described in `Mikolov, et. al: "Distributed Representations of Words and Phrases and their Compositionality" `_. To use your own custom ``scoring`` function, pass in a function with the following signature: * ``worda_count`` - number of corpus occurrences in `sentences` of the first token in the bigram being scored * ``wordb_count`` - number of corpus occurrences in `sentences` of the second token in the bigram being scored * ``bigram_count`` - number of occurrences in `sentences` of the whole bigram * ``len_vocab`` - the number of unique tokens in `sentences` * ``min_count`` - the `min_count` setting of the Phrases class * ``corpus_word_count`` - the total number of tokens (non-unique) in `sentences` The scoring function must accept all these parameters, even if it doesn't use them in its scoring. The scoring function **must be pickleable**. """ super().__init__(connector_words=connector_words) if min_count <= 0: raise ValueError("min_count should be at least 1") if threshold <= 0 and scoring == 'default': raise ValueError("threshold should be positive for default scoring") if scoring == 'npmi' and (threshold < -1 or threshold > 1): raise ValueError("threshold should be between -1 and 1 for npmi scoring") # Set scoring based on string. # Intentially override the value of the scoring parameter rather than set self.scoring here, # to still run the check of scoring function parameters in the next code block. if isinstance(scoring, str): if scoring == 'default': scoring = original_scorer elif scoring == 'npmi': scoring = npmi_scorer else: raise ValueError(f'unknown scoring method string {scoring} specified') scoring_params = [ 'worda_count', 'wordb_count', 'bigram_count', 'len_vocab', 'min_count', 'corpus_word_count', ] if callable(scoring): missing = [param for param in scoring_params if param not in getargspec(scoring)[0]] if not missing: self.scoring = scoring else: raise ValueError(f'scoring function missing expected parameters {missing}') self.min_count = min_count self.threshold = threshold self.max_vocab_size = max_vocab_size self.vocab = {} # mapping between token => its count self.min_reduce = 1 # ignore any tokens with count smaller than this self.delimiter = delimiter self.progress_per = progress_per self.corpus_word_count = 0 # Ensure picklability of the scorer. try: pickle.loads(pickle.dumps(self.scoring)) except pickle.PickleError: raise pickle.PickleError(f'Custom scoring function in {self.__class__.__name__} must be pickle-able') if sentences is not None: start = time.time() self.add_vocab(sentences) self.add_lifecycle_event("created", msg=f"built {self} in {time.time() - start:.2f}s") def __str__(self): return "%s<%i vocab, min_count=%s, threshold=%s, max_vocab_size=%s>" % ( self.__class__.__name__, len(self.vocab), self.min_count, self.threshold, self.max_vocab_size, ) @staticmethod def _learn_vocab(sentences, max_vocab_size, delimiter, connector_words, progress_per): """Collect unigram and bigram counts from the `sentences` iterable.""" sentence_no, total_words, min_reduce = -1, 0, 1 vocab = {} logger.info("collecting all words and their counts") for sentence_no, sentence in enumerate(sentences): if sentence_no % progress_per == 0: logger.info( "PROGRESS: at sentence #%i, processed %i words and %i word types", sentence_no, total_words, len(vocab), ) start_token, in_between = None, [] for word in sentence: if word not in connector_words: vocab[word] = vocab.get(word, 0) + 1 if start_token is not None: phrase_tokens = itertools.chain([start_token], in_between, [word]) joined_phrase_token = delimiter.join(phrase_tokens) vocab[joined_phrase_token] = vocab.get(joined_phrase_token, 0) + 1 start_token, in_between = word, [] # treat word as both end of a phrase AND beginning of another elif start_token is not None: in_between.append(word) total_words += 1 if len(vocab) > max_vocab_size: utils.prune_vocab(vocab, min_reduce) min_reduce += 1 logger.info( "collected %i token types (unigram + bigrams) from a corpus of %i words and %i sentences", len(vocab), total_words, sentence_no + 1, ) return min_reduce, vocab, total_words def add_vocab(self, sentences): """Update model parameters with new `sentences`. Parameters ---------- sentences : iterable of list of str Text corpus to update this model's parameters from. Example ------- .. sourcecode:: pycon >>> from gensim.test.utils import datapath >>> from gensim.models.word2vec import Text8Corpus >>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS >>> >>> # Train a phrase detector from a text corpus. >>> sentences = Text8Corpus(datapath('testcorpus.txt')) >>> phrases = Phrases(sentences, connector_words=ENGLISH_CONNECTOR_WORDS) # train model >>> assert len(phrases.vocab) == 37 >>> >>> more_sentences = [ ... [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there'], ... [u'machine', u'learning', u'can', u'be', u'new', u'york', u'sometimes'], ... ] >>> >>> phrases.add_vocab(more_sentences) # add new sentences to model >>> assert len(phrases.vocab) == 60 """ # Uses a separate vocab to collect the token counts from `sentences`. # This consumes more RAM than merging new sentences into `self.vocab` # directly, but gives the new sentences a fighting chance to collect # sufficient counts, before being pruned out by the (large) accumulated # counts collected in previous learn_vocab runs. min_reduce, vocab, total_words = self._learn_vocab( sentences, max_vocab_size=self.max_vocab_size, delimiter=self.delimiter, progress_per=self.progress_per, connector_words=self.connector_words, ) self.corpus_word_count += total_words if self.vocab: logger.info("merging %i counts into %s", len(vocab), self) self.min_reduce = max(self.min_reduce, min_reduce) for word, count in vocab.items(): self.vocab[word] = self.vocab.get(word, 0) + count if len(self.vocab) > self.max_vocab_size: utils.prune_vocab(self.vocab, self.min_reduce) self.min_reduce += 1 else: # Optimization for a common case: the current vocab is empty, so apply # the new vocab directly, no need to double it in memory. self.vocab = vocab logger.info("merged %s", self) def score_candidate(self, word_a, word_b, in_between): # Micro optimization: check for quick early-out conditions, before the actual scoring. word_a_cnt = self.vocab.get(word_a, 0) if word_a_cnt <= 0: return None, None word_b_cnt = self.vocab.get(word_b, 0) if word_b_cnt <= 0: return None, None phrase = self.delimiter.join([word_a] + in_between + [word_b]) # XXX: Why do we care about *all* phrase tokens? Why not just score the start+end bigram? phrase_cnt = self.vocab.get(phrase, 0) if phrase_cnt <= 0: return None, None score = self.scoring( worda_count=word_a_cnt, wordb_count=word_b_cnt, bigram_count=phrase_cnt, len_vocab=len(self.vocab), min_count=self.min_count, corpus_word_count=self.corpus_word_count, ) if score <= self.threshold: return None, None return phrase, score def freeze(self): """ Return an object that contains the bare minimum of information while still allowing phrase detection. See :class:`~gensim.models.phrases.FrozenPhrases`. Use this "frozen model" to dramatically reduce RAM footprint if you don't plan to make any further changes to your `Phrases` model. Returns ------- :class:`~gensim.models.phrases.FrozenPhrases` Exported object that's smaller, faster, but doesn't support model updates. """ return FrozenPhrases(self) def export_phrases(self): """Extract all found phrases. Returns ------ dict(str, float) Mapping between phrases and their scores. """ result, source_vocab = {}, self.vocab for token in source_vocab: unigrams = token.split(self.delimiter) if len(unigrams) < 2: continue # no phrases here phrase, score = self.score_candidate(unigrams[0], unigrams[-1], unigrams[1:-1]) if score is not None: result[phrase] = score return result class FrozenPhrases(_PhrasesTransformation): """Minimal state & functionality exported from a trained :class:`~gensim.models.phrases.Phrases` model. The goal of this class is to cut down memory consumption of `Phrases`, by discarding model state not strictly needed for the phrase detection task. Use this instead of `Phrases` if you do not need to update the bigram statistics with new documents any more. """ def __init__(self, phrases_model): """ Parameters ---------- phrases_model : :class:`~gensim.models.phrases.Phrases` Trained phrases instance, to extract all phrases from. Notes ----- After the one-time initialization, a :class:`~gensim.models.phrases.FrozenPhrases` will be much smaller and faster than using the full :class:`~gensim.models.phrases.Phrases` model. Examples ---------- .. sourcecode:: pycon >>> from gensim.test.utils import datapath >>> from gensim.models.word2vec import Text8Corpus >>> from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS >>> >>> # Load corpus and train a model. >>> sentences = Text8Corpus(datapath('testcorpus.txt')) >>> phrases = Phrases(sentences, min_count=1, threshold=1, connector_words=ENGLISH_CONNECTOR_WORDS) >>> >>> # Export a FrozenPhrases object that is more efficient but doesn't allow further training. >>> frozen_phrases = phrases.freeze() >>> print(frozen_phrases[sent]) [u'trees_graph', u'minors'] """ self.threshold = phrases_model.threshold self.min_count = phrases_model.min_count self.delimiter = phrases_model.delimiter self.scoring = phrases_model.scoring self.connector_words = phrases_model.connector_words logger.info('exporting phrases from %s', phrases_model) start = time.time() self.phrasegrams = phrases_model.export_phrases() self.add_lifecycle_event("created", msg=f"exported {self} from {phrases_model} in {time.time() - start:.2f}s") def __str__(self): return "%s<%i phrases, min_count=%s, threshold=%s>" % ( self.__class__.__name__, len(self.phrasegrams), self.min_count, self.threshold, ) def score_candidate(self, word_a, word_b, in_between): phrase = self.delimiter.join([word_a] + in_between + [word_b]) score = self.phrasegrams.get(phrase, NEGATIVE_INFINITY) if score > self.threshold: return phrase, score return None, None Phraser = FrozenPhrases # alias for backward compatibility