From 7e642a28d25f21dffa2ecee04500271822b1fd13 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Wed, 4 Dec 2019 17:54:09 -0800 Subject: [PATCH 01/60] slim low-value warnings --- gensim/models/base_any2vec.py | 4 -- gensim/test/test_fasttext.py | 92 +++++++++++++++++------------------ 2 files changed, 46 insertions(+), 50 deletions(-) diff --git a/gensim/models/base_any2vec.py b/gensim/models/base_any2vec.py index ba7e941f57..4a58257cf0 100644 --- a/gensim/models/base_any2vec.py +++ b/gensim/models/base_any2vec.py @@ -1367,10 +1367,6 @@ def _log_train_end(self, raw_word_count, trained_word_count, total_elapsed, job_ "training on a %i raw words (%i effective words) took %.1fs, %.0f effective words/s", raw_word_count, trained_word_count, total_elapsed, trained_word_count / total_elapsed ) - if job_tally < 10 * self.workers: - logger.warning( - "under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay" - ) # for backward compatibility @deprecated("Method will be removed in 4.0.0, use self.wv.most_similar() instead") diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index 3517a355a9..e117d5f283 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -70,17 +70,17 @@ def setUp(self): self.test_new_model_file = datapath('lee_fasttext_new.bin') def test_training(self): - model = FT_gensim(size=10, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model = FT_gensim(size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) model.build_vocab(sentences) self.model_sanity(model) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) - self.assertEqual(model.wv.vectors.shape, (12, 10)) + self.assertEqual(model.wv.vectors.shape, (12, 12)) self.assertEqual(len(model.wv.vocab), 12) - self.assertEqual(model.wv.vectors_vocab.shape[1], 10) - self.assertEqual(model.wv.vectors_ngrams.shape[1], 10) + self.assertEqual(model.wv.vectors_vocab.shape[1], 12) + self.assertEqual(model.wv.vectors_ngrams.shape[1], 12) self.model_sanity(model) # test querying for "most similar" by vector @@ -90,19 +90,19 @@ def test_training(self): self.assertEqual(sims, sims2) # build vocab and train in one step; must be the same as above - model2 = FT_gensim(sentences, size=10, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model2 = FT_gensim(sentences, size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) self.models_equal(model, model2) # verify oov-word vector retrieval invocab_vec = model.wv['minors'] # invocab word - self.assertEqual(len(invocab_vec), 10) + self.assertEqual(len(invocab_vec), 12) oov_vec = model.wv['minor'] # oov word - self.assertEqual(len(oov_vec), 10) + self.assertEqual(len(oov_vec), 12) def testFastTextTrainParameters(self): - model = FT_gensim(size=10, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model = FT_gensim(size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) model.build_vocab(sentences=sentences) self.assertRaises(TypeError, model.train, corpus_file=11111) @@ -115,17 +115,17 @@ def test_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: utils.save_as_line_sentence(sentences, corpus_file) - model = FT_gensim(size=10, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model = FT_gensim(size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) model.build_vocab(corpus_file=corpus_file) self.model_sanity(model) model.train(corpus_file=corpus_file, total_words=model.corpus_total_words, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) - self.assertEqual(model.wv.vectors.shape, (12, 10)) + self.assertEqual(model.wv.vectors.shape, (12, 12)) self.assertEqual(len(model.wv.vocab), 12) - self.assertEqual(model.wv.vectors_vocab.shape[1], 10) - self.assertEqual(model.wv.vectors_ngrams.shape[1], 10) + self.assertEqual(model.wv.vectors_vocab.shape[1], 12) + self.assertEqual(model.wv.vectors_ngrams.shape[1], 12) self.model_sanity(model) # test querying for "most similar" by vector @@ -136,14 +136,14 @@ def test_training_fromfile(self): # verify oov-word vector retrieval invocab_vec = model.wv['minors'] # invocab word - self.assertEqual(len(invocab_vec), 10) + self.assertEqual(len(invocab_vec), 12) oov_vec = model.wv['minor'] # oov word - self.assertEqual(len(oov_vec), 10) + self.assertEqual(len(oov_vec), 12) def models_equal(self, model, model2): self.assertEqual(len(model.wv.vocab), len(model2.wv.vocab)) - self.assertEqual(model.wv.num_ngram_vectors, model2.wv.num_ngram_vectors) + self.assertEqual(model.wv.bucket, model2.wv.bucket) self.assertTrue(np.allclose(model.wv.vectors_vocab, model2.wv.vectors_vocab)) self.assertTrue(np.allclose(model.wv.vectors_ngrams, model2.wv.vectors_ngrams)) self.assertTrue(np.allclose(model.wv.vectors, model2.wv.vectors)) @@ -199,7 +199,7 @@ def test_norm_vectors_not_saved(self): def model_sanity(self, model): self.assertEqual(model.wv.vectors.shape, (len(model.wv.vocab), model.vector_size)) self.assertEqual(model.wv.vectors_vocab.shape, (len(model.wv.vocab), model.vector_size)) - self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.num_ngram_vectors, model.vector_size)) + self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model.vector_size)) def test_load_fasttext_format(self): try: @@ -209,7 +209,7 @@ def test_load_fasttext_format(self): vocab_size, model_size = 1762, 10 self.assertEqual(model.wv.vectors.shape, (vocab_size, model_size)) self.assertEqual(len(model.wv.vocab), vocab_size, model_size) - self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.num_ngram_vectors, model_size)) + self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model_size)) expected_vec = [ -0.57144, @@ -252,7 +252,7 @@ def test_load_fasttext_format(self): self.assertEqual(model.wv.max_n, 6) self.assertEqual(model.wv.min_n, 3) self.assertEqual(model.wv.vectors.shape, (len(model.wv.vocab), model.vector_size)) - self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.num_ngram_vectors, model.vector_size)) + self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model.vector_size)) def test_load_fasttext_new_format(self): try: @@ -262,7 +262,7 @@ def test_load_fasttext_new_format(self): vocab_size, model_size = 1763, 10 self.assertEqual(new_model.wv.vectors.shape, (vocab_size, model_size)) self.assertEqual(len(new_model.wv.vocab), vocab_size, model_size) - self.assertEqual(new_model.wv.vectors_ngrams.shape, (new_model.wv.num_ngram_vectors, model_size)) + self.assertEqual(new_model.wv.vectors_ngrams.shape, (new_model.wv.bucket, model_size)) expected_vec = [ -0.025627, @@ -305,7 +305,7 @@ def test_load_fasttext_new_format(self): self.assertEqual(new_model.wv.max_n, 6) self.assertEqual(new_model.wv.min_n, 3) self.assertEqual(new_model.wv.vectors.shape, (len(new_model.wv.vocab), new_model.vector_size)) - self.assertEqual(new_model.wv.vectors_ngrams.shape, (new_model.wv.num_ngram_vectors, new_model.vector_size)) + self.assertEqual(new_model.wv.vectors_ngrams.shape, (new_model.wv.bucket, new_model.vector_size)) def test_load_model_supervised(self): with self.assertRaises(NotImplementedError): @@ -405,7 +405,7 @@ def test_wm_distance(self): def test_cbow_hs_training(self): model_gensim = FT_gensim( - size=50, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, + size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) @@ -434,7 +434,7 @@ def test_cbow_hs_training(self): def test_cbow_hs_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=50, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, + size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) @@ -467,7 +467,7 @@ def test_cbow_hs_training_fromfile(self): def test_sg_hs_training(self): model_gensim = FT_gensim( - size=50, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, + size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) @@ -496,7 +496,7 @@ def test_sg_hs_training(self): def test_sg_hs_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=50, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, + size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) @@ -529,7 +529,7 @@ def test_sg_hs_training_fromfile(self): def test_cbow_neg_training(self): model_gensim = FT_gensim( - size=50, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, + size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) @@ -558,7 +558,7 @@ def test_cbow_neg_training(self): def test_cbow_neg_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=50, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, + size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) @@ -591,7 +591,7 @@ def test_cbow_neg_training_fromfile(self): def test_sg_neg_training(self): model_gensim = FT_gensim( - size=50, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, + size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) @@ -620,7 +620,7 @@ def test_sg_neg_training(self): def test_sg_neg_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=50, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, + size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) @@ -651,7 +651,7 @@ def test_sg_neg_training_fromfile(self): self.assertGreaterEqual(overlap_count, 2) def test_online_learning(self): - model_hs = FT_gensim(sentences, size=10, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) + model_hs = FT_gensim(sentences, size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) self.assertTrue(len(model_hs.wv.vocab), 12) self.assertTrue(model_hs.wv.vocab['graph'].count, 3) model_hs.build_vocab(new_sentences, update=True) # update vocab @@ -666,7 +666,7 @@ def test_online_learning_fromfile(self): utils.save_as_line_sentence(new_sentences, new_corpus_file) model_hs = FT_gensim( - corpus_file=corpus_file, size=10, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) + corpus_file=corpus_file, size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) self.assertTrue(len(model_hs.wv.vocab), 12) self.assertTrue(model_hs.wv.vocab['graph'].count, 3) model_hs.build_vocab(corpus_file=new_corpus_file, update=True) # update vocab @@ -676,7 +676,7 @@ def test_online_learning_fromfile(self): def test_online_learning_after_save(self): tmpf = get_tmpfile('gensim_fasttext.tst') - model_neg = FT_gensim(sentences, size=10, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) + model_neg = FT_gensim(sentences, size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) model_neg.save(tmpf) model_neg = FT_gensim.load(tmpf) self.assertTrue(len(model_neg.wv.vocab), 12) @@ -692,7 +692,7 @@ def test_online_learning_after_save_fromfile(self): tmpf = get_tmpfile('gensim_fasttext.tst') model_neg = FT_gensim( - corpus_file=corpus_file, size=10, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) + corpus_file=corpus_file, size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) model_neg.save(tmpf) model_neg = FT_gensim.load(tmpf) self.assertTrue(len(model_neg.wv.vocab), 12) @@ -746,7 +746,7 @@ def test_cbow_neg_online(self): self.online_sanity(model) def test_get_vocab_word_vecs(self): - model = FT_gensim(size=10, min_count=1, seed=42, bucket=BUCKET) + model = FT_gensim(size=12, min_count=1, seed=42, bucket=BUCKET) model.build_vocab(sentences) original_syn0_vocab = np.copy(model.wv.vectors_vocab) model.wv.adjust_vectors() @@ -755,30 +755,30 @@ def test_get_vocab_word_vecs(self): def test_persistence_word2vec_format(self): """Test storing/loading the model in word2vec format.""" tmpf = get_tmpfile('gensim_fasttext_w2v_format.tst') - model = FT_gensim(sentences, min_count=1, size=10, bucket=BUCKET) + model = FT_gensim(sentences, min_count=1, size=12, bucket=BUCKET) model.wv.save_word2vec_format(tmpf, binary=True) loaded_model_kv = Word2VecKeyedVectors.load_word2vec_format(tmpf, binary=True) self.assertEqual(len(model.wv.vocab), len(loaded_model_kv.vocab)) self.assertTrue(np.allclose(model.wv['human'], loaded_model_kv['human'])) def test_bucket_ngrams(self): - model = FT_gensim(size=10, min_count=1, bucket=20) + model = FT_gensim(size=12, min_count=1, bucket=20) model.build_vocab(sentences) - self.assertEqual(model.wv.vectors_ngrams.shape, (20, 10)) + self.assertEqual(model.wv.vectors_ngrams.shape, (20, 12)) model.build_vocab(new_sentences, update=True) - self.assertEqual(model.wv.vectors_ngrams.shape, (20, 10)) + self.assertEqual(model.wv.vectors_ngrams.shape, (20, 12)) def test_estimate_memory(self): - model = FT_gensim(sg=1, hs=1, size=10, negative=5, min_count=3, bucket=BUCKET) + model = FT_gensim(sg=1, hs=1, size=12, negative=5, min_count=3, bucket=BUCKET) model.build_vocab(sentences) report = model.estimate_memory() self.assertEqual(report['vocab'], 2800) - self.assertEqual(report['syn0_vocab'], 160) - self.assertEqual(report['syn1'], 160) - self.assertEqual(report['syn1neg'], 160) - self.assertEqual(report['syn0_ngrams'], 2240) + self.assertEqual(report['syn0_vocab'], 192) + self.assertEqual(report['syn1'], 192) + self.assertEqual(report['syn1neg'], 192) + self.assertEqual(report['syn0_ngrams'], 2688) self.assertEqual(report['buckets_word'], 640) - self.assertEqual(report['total'], 6160) + self.assertEqual(report['total'], 6704) @unittest.skipIf(IS_WIN32, "avoid memory error with Appveyor x32") def testLoadOldModel(self): @@ -834,7 +834,7 @@ def test_cbow_hs_against_wrapper(self): loss='hs', sample=1e-3, negative=0, iter=5, min_n=3, max_n=6, sorted_vocab=1, threads=12) - model_gensim = FT_gensim(size=50, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, + model_gensim = FT_gensim(size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) @@ -855,7 +855,7 @@ def test_sg_hs_against_wrapper(self): loss='hs', sample=1e-3, negative=0, iter=5, min_n=3, max_n=6, sorted_vocab=1, threads=12) - model_gensim = FT_gensim(size=50, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, + model_gensim = FT_gensim(size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) @@ -1028,7 +1028,7 @@ def test_sanity(self): # # Only if match_gensim=True in init_post_load # - # self.assertEqual(trained.num_ngram_vectors, native.num_ngram_vectors) + # self.assertEqual(trained.bucket, native.bucket) compare_wv(trained.wv, native.wv, self) compare_vocabulary(trained.vocabulary, native.vocabulary, self) From b8de987b107ac994fe780c41e248b17cdf2b52be Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Wed, 4 Dec 2019 18:32:32 -0800 Subject: [PATCH 02/60] clarify vectors/vectors_vocab relationship; fix lockf & nonsense ngram-norming confusion --- MANIFEST.in | 2 - gensim/models/__init__.py | 2 +- gensim/models/_utils_any2vec.pyx | 147 ---- gensim/models/doc2vec.py | 6 +- gensim/models/fasttext.py | 709 ++++++++++++++--- gensim/models/fasttext_inner.pyx | 128 +++ gensim/models/keyedvectors.py | 1269 +++++++----------------------- gensim/models/poincare.py | 5 +- gensim/models/utils_any2vec.py | 298 ------- gensim/models/word2vec.py | 20 +- gensim/similarities/__init__.py | 1 + gensim/similarities/termsim.py | 41 + gensim/test/test_fasttext.py | 72 +- gensim/test/test_keyedvectors.py | 193 +---- gensim/test/test_similarities.py | 46 ++ setup.py | 1 - 16 files changed, 1205 insertions(+), 1735 deletions(-) delete mode 100644 gensim/models/_utils_any2vec.pyx delete mode 100644 gensim/models/utils_any2vec.py diff --git a/MANIFEST.in b/MANIFEST.in index 2ad20ee9f8..8aa14d25b8 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -28,8 +28,6 @@ include gensim/models/fasttext_inner.pxd include gensim/models/fasttext_corpusfile.cpp include gensim/models/fasttext_corpusfile.pyx -include gensim/models/_utils_any2vec.c -include gensim/models/_utils_any2vec.pyx include gensim/corpora/_mmreader.c include gensim/corpora/_mmreader.pyx include gensim/_matutils.c diff --git a/gensim/models/__init__.py b/gensim/models/__init__.py index a0ee690550..96ca698b27 100644 --- a/gensim/models/__init__.py +++ b/gensim/models/__init__.py @@ -13,7 +13,7 @@ from .logentropy_model import LogEntropyModel # noqa:F401 from .word2vec import Word2Vec # noqa:F401 from .doc2vec import Doc2Vec # noqa:F401 -from .keyedvectors import KeyedVectors, WordEmbeddingSimilarityIndex # noqa:F401 +from .keyedvectors import KeyedVectors # noqa:F401 from .ldamulticore import LdaMulticore # noqa:F401 from .phrases import Phrases # noqa:F401 from .normmodel import NormModel # noqa:F401 diff --git a/gensim/models/_utils_any2vec.pyx b/gensim/models/_utils_any2vec.pyx deleted file mode 100644 index cc4ba9bbb4..0000000000 --- a/gensim/models/_utils_any2vec.pyx +++ /dev/null @@ -1,147 +0,0 @@ -#!/usr/bin/env cython -# cython: boundscheck=False -# cython: wraparound=False -# cython: cdivision=True -# cython: embedsignature=True -# coding: utf-8 - -"""General functions used for any2vec models.""" - -# -# This is here to support older versions of the MSVC compiler that don't have stdint.h. -# -cdef extern from "stdint_wrapper.h": - ctypedef unsigned int uint32_t - ctypedef signed char int8_t - -from six import PY2 -import numpy as np -cimport numpy as np - - -cpdef ft_hash_bytes(bytes bytez): - """Calculate hash based on `bytez`. - Reproduce `hash method from Facebook fastText implementation - `_. - - Parameters - ---------- - bytez : bytes - The string whose hash needs to be calculated, encoded as UTF-8. - - Returns - ------- - unsigned int - The hash of the string. - - """ - cdef uint32_t h = 2166136261 - cdef char b - - for b in bytez: - h = h ^ (b) - h = h * 16777619 - return h - - -cpdef ft_hash_broken(unicode string): - """Calculate hash based on `string`. - - This implementation is broken, see https://github.com/RaRe-Technologies/gensim/issues/2059. - It is here only for maintaining backwards compatibility with older models. - - Parameters - ---------- - string : unicode - The string whose hash needs to be calculated. - - Returns - ------- - unsigned int - The hash of the string. - - """ - cdef unsigned int h = 2166136261 - for c in string: - h ^= ord(c) - h *= 16777619 - return h - - -cpdef compute_ngrams(word, unsigned int min_n, unsigned int max_n): - """Get the list of all possible ngrams for a given word. - - Parameters - ---------- - word : str - The word whose ngrams need to be computed. - min_n : unsigned int - Minimum character length of the ngrams. - max_n : unsigned int - Maximum character length of the ngrams. - - Returns - ------- - list of str - Sequence of character ngrams. - - """ - cdef unicode extended_word = f'<{word}>' - ngrams = [] - for ngram_length in range(min_n, min(len(extended_word), max_n) + 1): - for i in range(0, len(extended_word) - ngram_length + 1): - ngrams.append(extended_word[i:i + ngram_length]) - return ngrams - -# -# UTF-8 bytes that begin with 10 are subsequent bytes of a multi-byte sequence, -# as opposed to a new character. -# -cdef unsigned char _MB_MASK = 0xC0 -cdef unsigned char _MB_START = 0x80 - - -cpdef compute_ngrams_bytes(word, unsigned int min_n, unsigned int max_n): - """Computes ngrams for a word. - - Ported from the original FB implementation. - - Parameters - ---------- - word : str - A unicode string. - min_n : unsigned int - The minimum ngram length. - max_n : unsigned int - The maximum ngram length. - - Returns: - -------- - list of str - A list of ngrams, where each ngram is a list of **bytes**. - - See Also - -------- - `Original implementation `__ - - """ - cdef bytes utf8_word = ('<%s>' % word).encode("utf-8") - cdef const unsigned char *bytez = utf8_word - cdef size_t num_bytes = len(utf8_word) - cdef size_t j, i, n - - ngrams = [] - for i in range(num_bytes): - if bytez[i] & _MB_MASK == _MB_START: - continue - - j, n = i, 1 - while j < num_bytes and n <= max_n: - j += 1 - while j < num_bytes and (bytez[j] & _MB_MASK) == _MB_START: - j += 1 - if n >= min_n and not (n == 1 and (i == 0 or j == num_bytes)): - ngram = bytes(bytez[i:j]) - ngrams.append(ngram) - n += 1 - return ngrams diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index c5cb5b40a2..f7c1e55cdd 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -79,7 +79,7 @@ from gensim.utils import call_on_class_only, deprecated from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc -from gensim.models.word2vec import Word2VecKeyedVectors, Word2VecVocab, Word2VecTrainables +from gensim.models.word2vec import KeyedVectors, Word2VecVocab, Word2VecTrainables from gensim.models.word2vec import train_cbow_pair, train_sg_pair, train_batch_sg # noqa from six.moves import range from six import string_types, integer_types, itervalues @@ -183,7 +183,7 @@ class Doc2Vec(BaseWordEmbeddingsModel): Attributes ---------- - wv : :class:`~gensim.models.keyedvectors.Word2VecKeyedVectors` + wv : :class:`~gensim.models.keyedvectors.KeyedVectors` This object essentially contains the mapping between words and embeddings. After training, it can be used directly to query those embeddings in various ways. See the module level docstring for examples. @@ -342,7 +342,7 @@ def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_wo dm=dm, dm_concat=dm_concat, dm_tag_count=dm_tag_count, vector_size=self.vector_size, **trainables_kwargs) - self.wv = Word2VecKeyedVectors(self.vector_size) + self.wv = KeyedVectors(self.vector_size) self.docvecs = docvecs or Doc2VecKeyedVectors(self.vector_size, docvecs_mapfile) self.comment = comment diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index d2da493ec9..79035323fc 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -43,7 +43,7 @@ Once you have a model, you can access its keyed vectors via the `model.wv` attributes. The keyed vectors instance is quite powerful: it can perform a wide range of NLP tasks. -For a full list of examples, see :class:`~gensim.models.keyedvectors.FastTextKeyedVectors`. +For a full list of examples, see :class:`~gensim.models.keyedvectors.KeyedVectors`. You can also pass all the above parameters to the constructor to do everything in a single line: @@ -257,12 +257,11 @@ The implementation is split across several submodules: - :mod:`gensim.models.fasttext`: This module. Contains FastText-specific functionality only. -- :mod:`gensim.models.keyedvectors`: Implements both generic and FastText-specific functionality. +- :mod:`gensim.models.keyedvectors`: Implements generic functionality. - :mod:`gensim.models.word2vec`: Contains implementations for the vocabulary and the trainables for FastText. - :mod:`gensim.models.base_any2vec`: Contains implementations for the base. classes, including functionality such as callbacks, logging. -- :mod:`gensim.models.utils_any2vec`: Wrapper over Cython extensions. - :mod:`gensim.utils`: Implements model I/O (loading and saving). Our implementation relies heavily on inheritance. @@ -271,7 +270,7 @@ - :class:`~gensim.models.word2vec.Word2VecVocab`: the vocabulary. Keeps track of all the unique words, sometimes discarding the extremely rare ones. This is sometimes called the Dictionary within Gensim. -- :class:`~gensim.models.keyedvectors.FastTextKeyedVectors`: the vectors. +- :class:`~gensim.models.fasttext.FastTextKeyedVectors`: the vectors. Once training is complete, this class is sufficient for calculating embeddings. - :class:`~gensim.models.fasttext.FastTextTrainables`: the underlying neural network. The implementation uses this class to *learn* the word embeddings. @@ -289,13 +288,11 @@ import gensim.models._fasttext_bin -from gensim.models.word2vec import Word2VecVocab, Word2VecTrainables, train_sg_pair, train_cbow_pair # noqa -from gensim.models.keyedvectors import FastTextKeyedVectors +from gensim.models.word2vec import Word2VecVocab, Word2VecTrainables +from gensim.models.keyedvectors import KeyedVectors, _save_word2vec_format from gensim.models.base_any2vec import BaseWordEmbeddingsModel -from gensim.models.utils_any2vec import ft_ngram_hashes +from gensim.utils import deprecated, call_on_class_only, open, NO_CYTHON -from gensim import utils -from gensim.utils import deprecated, call_on_class_only logger = logging.getLogger(__name__) @@ -305,10 +302,14 @@ train_batch_cbow, FAST_VERSION, MAX_WORDS_IN_BATCH, + compute_ngrams, + compute_ngrams_bytes, + ft_hash_broken, + ft_hash_bytes, ) from gensim.models.fasttext_corpusfile import train_epoch_sg, train_epoch_cbow except ImportError: - raise utils.NO_CYTHON + raise NO_CYTHON class FastText(BaseWordEmbeddingsModel): @@ -321,7 +322,7 @@ class FastText(BaseWordEmbeddingsModel): Attributes ---------- - wv : :class:`~gensim.models.keyedvectors.FastTextKeyedVectors` + wv : :class:`~gensim.models.fasttext.FastTextKeyedVectors` This object essentially contains the mapping between words and embeddings. These are similar to the embeddings computed in the :class:`~gensim.models.word2vec.Word2Vec`, however here we also include vectors for n-grams. This allows the model to compute embeddings even for **unseen** words (that do not exist in the vocabulary), @@ -482,56 +483,6 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, size=100, alpha callbacks=callbacks, batch_words=batch_words, trim_rule=trim_rule, sg=sg, alpha=alpha, window=window, seed=seed, hs=hs, negative=negative, cbow_mean=cbow_mean, min_alpha=min_alpha) - @property - @deprecated("Attribute will be removed in 4.0.0, use wv.min_n instead") - def min_n(self): - return self.wv.min_n - - @property - @deprecated("Attribute will be removed in 4.0.0, use wv.max_n instead") - def max_n(self): - return self.wv.max_n - - @property - @deprecated("Attribute will be removed in 4.0.0, use trainables.bucket instead") - def bucket(self): - return self.trainables.bucket - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_vocab_lockf instead") - def syn0_vocab_lockf(self): - return self.trainables.vectors_vocab_lockf - - @syn0_vocab_lockf.setter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_vocab_lockf instead") - def syn0_vocab_lockf(self, value): - self.trainables.vectors_vocab_lockf = value - - @syn0_vocab_lockf.deleter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_vocab_lockf instead") - def syn0_vocab_lockf(self): - del self.trainables.vectors_vocab_lockf - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_ngrams_lockf instead") - def syn0_ngrams_lockf(self): - return self.trainables.vectors_ngrams_lockf - - @syn0_ngrams_lockf.setter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_ngrams_lockf instead") - def syn0_ngrams_lockf(self, value): - self.trainables.vectors_ngrams_lockf = value - - @syn0_ngrams_lockf.deleter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_ngrams_lockf instead") - def syn0_ngrams_lockf(self): - del self.trainables.vectors_ngrams_lockf - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.wv.num_ngram_vectors instead") - def num_ngram_vectors(self): - return self.wv.num_ngram_vectors - def build_vocab(self, sentences=None, corpus_file=None, update=False, progress_per=10000, keep_raw_vocab=False, trim_rule=None, **kwargs): """Build vocabulary from a sequence of sentences (can be a once-only generator stream). @@ -626,9 +577,8 @@ def _set_train_params(self, **kwargs): def _clear_post_train(self): """Clear the model's internal structures after training has finished to free up RAM.""" self.wv.vectors_norm = None - self.wv.vectors_vocab_norm = None - self.wv.vectors_ngrams_norm = None self.wv.buckets_word = None + self.wv.adjust_vectors() # ensure composite-word vecs reflect latest training def estimate_memory(self, vocab_size=None, report=None): vocab_size = vocab_size or len(self.wv.vocab) @@ -829,24 +779,6 @@ def clear_sims(self): """ self._clear_post_train() - @deprecated("Method will be removed in 4.0.0, use self.wv.__getitem__() instead") - def __getitem__(self, words): - """Deprecated. Use self.wv.__getitem__() instead. - - Refer to the documentation for :meth:`gensim.models.keyedvectors.KeyedVectors.__getitem__` - - """ - return self.wv.__getitem__(words) - - @deprecated("Method will be removed in 4.0.0, use self.wv.__contains__() instead") - def __contains__(self, word): - """Deprecated. Use self.wv.__contains__() instead. - - Refer to the documentation for :meth:`gensim.models.keyedvectors.KeyedVectors.__contains__` - - """ - return self.wv.__contains__(word) - @classmethod @deprecated( 'use load_facebook_vectors (to use pretrained embeddings) or load_facebook_model ' @@ -895,7 +827,7 @@ def save(self, *args, **kwargs): """ kwargs['ignore'] = kwargs.get( - 'ignore', ['vectors_norm', 'vectors_vocab_norm', 'vectors_ngrams_norm', 'buckets_word']) + 'ignore', ['vectors_norm', 'buckets_word']) super(FastText, self).save(*args, **kwargs) @classmethod @@ -922,9 +854,9 @@ def load(cls, *args, **kwargs): model = super(FastText, cls).load(*args, **kwargs) if not hasattr(model.trainables, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'): - model.trainables.vectors_vocab_lockf = ones(model.wv.vectors_vocab.shape, dtype=REAL) + model.trainables.vectors_vocab_lockf = ones(len(model.wv.vectors_vocab), dtype=REAL) if not hasattr(model.trainables, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'): - model.trainables.vectors_ngrams_lockf = ones(model.wv.vectors_ngrams.shape, dtype=REAL) + model.trainables.vectors_ngrams_lockf = ones(len(model.wv.vectors_ngrams), dtype=REAL) if not hasattr(model.wv, 'bucket'): model.wv.bucket = model.trainables.bucket @@ -933,15 +865,10 @@ def load(cls, *args, **kwargs): from gensim.models.deprecated.fasttext import load_old_fasttext model = load_old_fasttext(*args, **kwargs) - gensim.models.keyedvectors._try_upgrade(model.wv) + _try_upgrade(model.wv) return model - @deprecated("Method will be removed in 4.0.0, use self.wv.accuracy() instead") - def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_insensitive=True): - most_similar = most_similar or FastTextKeyedVectors.most_similar - return self.wv.accuracy(questions, restrict_vocab, most_similar, case_insensitive) - class FastTextVocab(Word2VecVocab): """This is a redundant class. It exists only to maintain backwards compatibility @@ -991,15 +918,13 @@ def __init__(self, vector_size=100, seed=1, hashfxn=hash, bucket=2000000): # 1. vectors_vocab_lockf # 2. vectors_ngrams_lockf # - # These are both 2D matrices of shapes equal to the shapes of + # These are both 1D matrices of shapes equal to the lengths of # wv.vectors_vocab and wv.vectors_ngrams. So, each row corresponds to - # a vector, and each column corresponds to a dimension within that - # vector. + # a vector. # # Lockf stands for "lock factor": zero values suppress learning, one - # values enable it. Interestingly, the vectors_vocab_lockf and - # vectors_ngrams_lockf seem to be used only by the C code in - # fasttext_inner.pyx. + # values enable it. The vectors_vocab_lockf and vectors_ngrams_lockf + # are used only by the Cython code in fasttext_inner.pyx. # # The word2vec implementation also uses vectors_lockf: in that case, # it's a 1D array, with a real number for each vector. The FastText @@ -1017,7 +942,7 @@ def init_ngrams_weights(self, wv, update=False, vocabulary=None): Parameters ---------- - wv : :class:`~gensim.models.keyedvectors.FastTextKeyedVectors` + wv : :class:`~gensim.models.fasttext.FastTextKeyedVectors` Contains the mapping between the words and embeddings. The vectors for the computed ngrams will go here. update : bool @@ -1030,12 +955,12 @@ def init_ngrams_weights(self, wv, update=False, vocabulary=None): """ if not update: wv.init_ngrams_weights(self.seed) - self.vectors_vocab_lockf = ones(wv.vectors_vocab.shape, dtype=REAL) - self.vectors_ngrams_lockf = ones(wv.vectors_ngrams.shape, dtype=REAL) + self.vectors_vocab_lockf = ones(len(wv.vectors_vocab), dtype=REAL) + self.vectors_ngrams_lockf = ones(len(wv.vectors_ngrams), dtype=REAL) else: wv.update_ngrams_weights(self.seed, vocabulary.old_vocab_len) - self.vectors_vocab_lockf = _pad_ones(self.vectors_vocab_lockf, wv.vectors_vocab.shape) - self.vectors_ngrams_lockf = _pad_ones(self.vectors_ngrams_lockf, wv.vectors_ngrams.shape) + self.vectors_vocab_lockf = _pad_ones(self.vectors_vocab_lockf, len(wv.vectors_vocab)) + self.vectors_ngrams_lockf = _pad_ones(self.vectors_ngrams_lockf, len(wv.vectors_ngrams)) def init_post_load(self, model, hidden_output): num_vectors = len(model.wv.vectors) @@ -1045,8 +970,8 @@ def init_post_load(self, model, hidden_output): assert num_vectors > 0, 'expected num_vectors to be initialized already' assert vocab_size > 0, 'expected vocab_size to be initialized already' - self.vectors_ngrams_lockf = ones(model.wv.vectors_ngrams.shape, dtype=REAL) - self.vectors_vocab_lockf = ones(model.wv.vectors_vocab.shape, dtype=REAL) + self.vectors_ngrams_lockf = ones(len(model.wv.vectors_ngrams), dtype=REAL) + self.vectors_vocab_lockf = ones(len(model.wv.vectors_vocab.shape), dtype=REAL) if model.hs: self.syn1 = hidden_output @@ -1056,15 +981,12 @@ def init_post_load(self, model, hidden_output): self.layer1_size = vector_size -def _pad_ones(m, new_shape): - """Pad a matrix with additional rows filled with ones.""" - assert m.shape[0] <= new_shape[0], 'the new number of rows must be greater' - assert m.shape[1] == new_shape[1], 'the number of columns must match' - new_rows = new_shape[0] - m.shape[0] - if new_rows == 0: - return m - suffix = ones((new_rows, m.shape[1]), dtype=REAL) - return vstack([m, suffix]) +def _pad_ones(m, new_len): + """Pad array with additional entries filled with ones.""" + assert len(m) <= new_len, 'the new number of rows %i must be greater than old %i' % (new_len, len(m)) + new_arr = np.ones(new_len, dtype=REAL) + new_arr[:len(m)] = m + return new_arr def load_facebook_model(path, encoding='utf-8'): @@ -1157,7 +1079,7 @@ def load_facebook_vectors(path, encoding='utf-8'): Returns ------- - gensim.models.keyedvectors.FastTextKeyedVectors + gensim.models.fasttext.FastTextKeyedVectors The word embeddings. Examples @@ -1208,7 +1130,7 @@ def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): The loaded model. """ - with utils.open(model_file, 'rb') as fin: + with open(model_file, 'rb') as fin: m = gensim.models._fasttext_bin.load(fin, encoding=encoding, full_model=full_model) model = FastText( @@ -1324,3 +1246,562 @@ def save_facebook_model(model, path, encoding="utf-8", lr_update_rate=100, word_ """ fb_fasttext_parameters = {"lr_update_rate": lr_update_rate, "word_ngrams": word_ngrams} gensim.models._fasttext_bin.save(model, path, fb_fasttext_parameters, encoding) + + +class FastTextKeyedVectors(KeyedVectors): + """Vectors and vocab for :class:`~gensim.models.fasttext.FastText`. + + Implements significant parts of the FastText algorithm. For example, + the :func:`word_vec` calculates vectors for out-of-vocabulary (OOV) + entities. FastText achieves this by keeping vectors for ngrams: + adding the vectors for the ngrams of an entity yields the vector for the + entity. + + Similar to a hashmap, this class keeps a fixed number of buckets, and + maps all ngrams to buckets using a hash function. + + This class also provides an abstraction over the hash functions used by + Gensim's FastText implementation over time. The hash function connects + ngrams to buckets. Originally, the hash function was broken and + incompatible with Facebook's implementation. The current hash is fully + compatible. + + Parameters + ---------- + vector_size : int + The dimensionality of all vectors. + min_n : int + The minimum number of characters in an ngram + max_n : int + The maximum number of characters in an ngram + bucket : int + The number of buckets. + compatible_hash : boolean + If True, uses the Facebook-compatible hash function instead of the + Gensim backwards-compatible hash function. + + Attributes + ---------- + vectors_vocab : np.array + Each row corresponds to a vector for an entity in the vocabulary. + Columns correspond to vector dimensions. When embedded in a full + FastText model, these are the full-word-token vectors updated + by training, whereas the inherited vectors are the actual per-word + vectors synthesized from the full-word-token and all subword (ngram) + vectors. + vectors_ngrams : np.array + A vector for each ngram across all entities in the vocabulary. + Each row is a vector that corresponds to a bucket. + Columns correspond to vector dimensions. + buckets_word : dict + Maps vocabulary items (by their index) to the buckets they occur in. + + """ + def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): + super(FastTextKeyedVectors, self).__init__(vector_size=vector_size) + self.vectors_vocab = None # fka syn0_vocab + self.vectors_ngrams = None # fka syn0_ngrams + self.buckets_word = None + self.min_n = min_n + self.max_n = max_n + self.bucket = bucket # count of buckets, fka num_ngram_vectors + self.compatible_hash = compatible_hash + + @classmethod + def load(cls, fname_or_handle, **kwargs): + model = super(FastTextKeyedVectors, cls).load(fname_or_handle, **kwargs) + if isinstance(model, FastTextKeyedVectors): + if not hasattr(model, 'compatible_hash'): + model.compatible_hash = False + _try_upgrade(model) + return model + + 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. + + Note + ---- + This method **always** returns True, because of the way FastText works. + + If you want to check if a word is an in-vocabulary term, use this instead: + + .. pycon: + + >>> from gensim.test.utils import datapath + >>> from gensim.models import FastText + >>> cap_path = datapath("crime-and-punishment.bin") + >>> model = FastText.load_fasttext_format(cap_path, full_model=False) + >>> 'steamtrain' in model.wv.vocab # If False, is an OOV term + False + + """ + return True + + def save(self, *args, **kwargs): + """Save object. + + Parameters + ---------- + fname : str + Path to the output file. + + See Also + -------- + :meth:`~gensim.models.fasttext.FastTextKeyedVectors.load` + Load object. + + """ + # don't bother storing the cached normalized vectors + ignore_attrs = [ + 'vectors_norm', + 'buckets_word', + 'hash2index', + ] + kwargs['ignore'] = kwargs.get('ignore', ignore_attrs) + super(FastTextKeyedVectors, self).save(*args, **kwargs) + + def get_vector(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).get_vector(word, use_norm) + elif self.bucket == 0: + raise KeyError('cannot calculate vector for OOV word without ngrams') + else: + word_vec = np.zeros(self.vectors_ngrams.shape[1], dtype=np.float32) + ngram_weights = self.vectors_ngrams + ngram_hashes = ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash) + if len(ngram_hashes) == 0: + # + # If it is impossible to extract _any_ ngrams from the input + # word, then the best we can do is return a vector that points + # to the origin. The reference FB implementation does this, + # too. + # + # https://github.com/RaRe-Technologies/gensim/issues/2402 + # + logger.warning('could not extract any ngrams from %r, returning origin vector', word) + return word_vec + for nh in ngram_hashes: + word_vec += ngram_weights[nh] + word_vec /= len(ngram_hashes) + if use_norm: + return word_vec / np.linalg.norm(word_vec) + else: + return word_vec + + 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) + + def init_ngrams_weights(self, seed): + """Initialize the vocabulary and ngrams weights prior to training. + + Creates the weight matrices and initializes them with uniform random values. + + Parameters + ---------- + seed : float + The seed for the PRNG. + + Note + ---- + Call this **after** the vocabulary has been fully initialized. + + """ + self.buckets_word = _process_fasttext_vocab( + self.vocab.items(), + self.min_n, + self.max_n, + self.bucket, + self.compatible_hash, + ) + + rand_obj = np.random + rand_obj.seed(seed) + + lo, hi = -1.0 / self.vector_size, 1.0 / self.vector_size + vocab_shape = (len(self.vocab), self.vector_size) + ngrams_shape = (self.bucket, self.vector_size) + self.vectors_vocab = rand_obj.uniform(lo, hi, vocab_shape).astype(REAL) + + # + # We could have initialized vectors_ngrams at construction time, but we + # do it here for two reasons: + # + # 1. The constructor does not have access to the random seed + # 2. We want to use the same rand_obj to fill vectors_vocab _and_ + # vectors_ngrams, and vectors_vocab cannot happen at construction + # time because the vocab is not initialized at that stage. + # + self.vectors_ngrams = rand_obj.uniform(lo, hi, ngrams_shape).astype(REAL) + + def update_ngrams_weights(self, seed, old_vocab_len): + """Update the vocabulary weights for training continuation. + + Parameters + ---------- + seed : float + The seed for the PRNG. + old_vocab_length : int + The length of the vocabulary prior to its update. + + Note + ---- + Call this **after** the vocabulary has been updated. + + """ + self.buckets_word = _process_fasttext_vocab( + self.vocab.items(), + self.min_n, + self.max_n, + self.bucket, + self.compatible_hash, + ) + + rand_obj = np.random + rand_obj.seed(seed) + + new_vocab = len(self.vocab) - old_vocab_len + self.vectors_vocab = _pad_random(self.vectors_vocab, new_vocab, rand_obj) + + def init_post_load(self, fb_vectors): + """Perform initialization after loading a native Facebook model. + + Expects that the vocabulary (self.vocab) has already been initialized. + + Parameters + ---------- + fb_vectors : np.array + A matrix containing vectors for all the entities, including words + and ngrams. This comes directly from the binary model. + The order of the vectors must correspond to the indices in + the vocabulary. + match_gensim : boolean, optional + No longer supported. + + """ + vocab_words = len(self.vocab) + assert fb_vectors.shape[0] == vocab_words + self.bucket, 'unexpected number of vectors' + assert fb_vectors.shape[1] == self.vector_size, 'unexpected vector dimensionality' + + # + # The incoming vectors contain vectors for both words AND + # ngrams. We split them into two separate matrices, because our + # implementation treats them differently. + # + self.vectors_vocab = np.array(fb_vectors[:vocab_words, :]) + self.vectors_ngrams = np.array(fb_vectors[vocab_words:, :]) + self.buckets_word = None # This can get initialized later + + self.adjust_vectors() # calculate composite full-word vectors + + def adjust_vectors(self): + """Adjust the vectors for words in the vocabulary. + + The adjustment composes the trained full-word-token vectors with + the vectors of the subword ngrams, matching the Facebook reference + implementation behavior. + + """ + if self.bucket == 0: + return + + self.vectors = self.vectors_vocab[:].copy() + for i, w in enumerate(self.index2key): + ngram_hashes = ft_ngram_hashes(w, self.min_n, self.max_n, self.bucket, self.compatible_hash) + for nh in ngram_hashes: + self.vectors[i] += self.vectors_ngrams[nh] + self.vectors[i] /= len(ngram_hashes) + 1 + +def _process_fasttext_vocab(iterable, min_n, max_n, num_buckets, compatible_hash): + """ + Performs a common operation for FastText weight initialization and + updates: scan the vocabulary, calculate ngrams and their hashes, keep + track of new ngrams, the buckets that each word relates to via its + ngrams, etc. + + Parameters + ---------- + iterable : list + A list of (word, :class:`Vocab`) tuples. + min_n : int + The minimum length of ngrams. + max_n : int + The maximum length of ngrams. + num_buckets : int + The number of buckets used by the model. + compatible_hash : boolean + True for compatibility with the Facebook implementation. + False for compatibility with the old Gensim implementation. + + Returns + ------- + dict + Keys are indices of entities in the vocabulary (words). Values are + arrays containing indices into vectors_ngrams for each ngram of the + word. + + """ + word_indices = {} + + if num_buckets == 0: + return {v.index: np.array([], dtype=np.uint32) for w, v in iterable} + + for word, vocab in iterable: + wi = [] + for ngram_hash in ft_ngram_hashes(word, min_n, max_n, num_buckets, compatible_hash): + wi.append(ngram_hash) + word_indices[vocab.index] = np.array(wi, dtype=np.uint32) + + return word_indices + + +def _pad_random(m, new_rows, rand): + """Pad a matrix with additional rows filled with random values.""" + rows, columns = m.shape + low, high = -1.0 / columns, 1.0 / columns + suffix = rand.uniform(low, high, (new_rows, columns)).astype(REAL) + return vstack([m, suffix]) + + +def _rollback_optimization(kv): + """Undo the optimization that pruned buckets. + + This unfortunate optimization saves memory and CPU cycles, but breaks + compatibility with Facebook's model by introducing divergent behavior + for OOV words. + + """ + logger.warning( + "This saved FastText model was trained with an optimization we no longer support. " + "The current Gensim version automatically reverses this optimization during loading. " + "Save the loaded model to a new file and reload to suppress this message." + ) + assert hasattr(kv, 'hash2index') + assert hasattr(kv, 'bucket') + + kv.vectors_ngrams = _unpack(kv.vectors_ngrams, kv.bucket, kv.hash2index) + + # + # We have replaced num_ngram_vectors with a property and deprecated it. + # We can't delete it because the new attribute masks the member. + # + del kv.hash2index + + +def _unpack_copy(m, num_rows, hash2index, seed=1): + """Same as _unpack, but makes a copy of the matrix. + + Simpler implementation, but uses more RAM. + + """ + rows, columns = m.shape + if rows == num_rows: + # + # Nothing to do. + # + return m + assert num_rows > rows + + rand_obj = np.random + rand_obj.seed(seed) + + n = np.empty((0, columns), dtype=m.dtype) + n = _pad_random(n, num_rows, rand_obj) + + for src, dst in hash2index.items(): + n[src] = m[dst] + + return n + + +def _unpack(m, num_rows, hash2index, seed=1): + """Restore the array to its natural shape, undoing the optimization. + + A packed matrix contains contiguous vectors for ngrams, as well as a hashmap. + The hash map maps the ngram hash to its index in the packed matrix. + To unpack the matrix, we need to do several things: + + 1. Restore the matrix to its "natural" shape, where the number of rows + equals the number of buckets. + 2. Rearrange the existing rows such that the hashmap becomes the identity + function and is thus redundant. + 3. Fill the new rows with random values. + + Parameters + ---------- + + m : np.ndarray + The matrix to restore. + num_rows : int + The number of rows that this array should have. + hash2index : dict + the product of the optimization we are undoing. + seed : float, optional + The seed for the PRNG. Will be used to initialize new rows. + + Returns + ------- + np.array + The unpacked matrix. + + Notes + ----- + + The unpacked matrix will reference some rows in the input matrix to save memory. + Throw away the old matrix after calling this function, or use np.copy. + + """ + orig_rows, orig_columns = m.shape + if orig_rows == num_rows: + # + # Nothing to do. + # + return m + assert num_rows > orig_rows + + rand_obj = np.random + rand_obj.seed(seed) + + # + # Rows at the top of the matrix (the first orig_rows) will contain "packed" learned vectors. + # Rows at the bottom of the matrix will be "free": initialized to random values. + # + m = _pad_random(m, num_rows - orig_rows, rand_obj) + + # + # Swap rows to transform hash2index into the identify function. + # There are two kinds of swaps. + # First, rearrange the rows that belong entirely within the original matrix dimensions. + # Second, swap out rows from the original matrix dimensions, replacing them with + # randomly initialized values. + # + # N.B. We only do the swap in one direction, because doing it in both directions + # nullifies the effect. + # + swap = {h: i for (h, i) in hash2index.items() if h < i < orig_rows} + swap.update({h: i for (h, i) in hash2index.items() if h >= orig_rows}) + for h, i in swap.items(): + assert h != i + m[[h, i]] = m[[i, h]] # swap rows i and h + + return m + + +def _try_upgrade(wv): + if hasattr(wv, 'hash2index'): + _rollback_optimization(wv) + + if not hasattr(wv, 'compatible_hash'): + logger.warning( + "This older model was trained with a buggy hash function. " + "The model will continue to work, but consider training it " + "from scratch." + ) + wv.compatible_hash = False + + +# +# UTF-8 bytes that begin with 10 are subsequent bytes of a multi-byte sequence, +# as opposed to a new character. +# +_MB_MASK = 0xC0 +_MB_START = 0x80 + + +def _byte_to_int_py3(b): + return b + + +def _byte_to_int_py2(b): + return ord(b) + + +_byte_to_int = _byte_to_int_py2 if six.PY2 else _byte_to_int_py3 + + +def _is_utf8_continue(b): + return _byte_to_int(b) & _MB_MASK == _MB_START + + +def ft_ngram_hashes(word, minn, maxn, num_buckets, fb_compatible=True): + """Calculate the ngrams of the word and hash them. + + Parameters + ---------- + word : str + The word to calculate ngram hashes for. + minn : int + Minimum ngram length + maxn : int + Maximum ngram length + num_buckets : int + The number of buckets + fb_compatible : boolean, optional + True for compatibility with the Facebook implementation. + False for compatibility with the old Gensim implementation. + + Returns + ------- + A list of hashes (integers), one per each detected ngram. + + """ + if fb_compatible: + encoded_ngrams = compute_ngrams_bytes(word, minn, maxn) + hashes = [ft_hash_bytes(n) % num_buckets for n in encoded_ngrams] + else: + text_ngrams = compute_ngrams(word, minn, maxn) + hashes = [ft_hash_broken(n) % num_buckets for n in text_ngrams] + return hashes + + +# BACKWARD COMPATIBILITY FOR OLDER PICKLES +from gensim.models import keyedvectors # noqa: F402 +keyedvectors.FastTextKeyedVectors = FastTextKeyedVectors diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index a413db8460..c2794d7d11 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -706,6 +706,134 @@ def train_batch_cbow(model, sentences, alpha, _work, _neu1): return num_words +cpdef ft_hash_bytes(bytes bytez): + """Calculate hash based on `bytez`. + Reproduce `hash method from Facebook fastText implementation + `_. + + Parameters + ---------- + bytez : bytes + The string whose hash needs to be calculated, encoded as UTF-8. + + Returns + ------- + unsigned int + The hash of the string. + + """ + cdef np.uint32_t h = 2166136261 + cdef char b + + for b in bytez: + h = h ^ (b) + h = h * 16777619 + return h + + +cpdef ft_hash_broken(unicode string): + """Calculate hash based on `string`. + + This implementation is broken, see https://github.com/RaRe-Technologies/gensim/issues/2059. + It is here only for maintaining backwards compatibility with older models. + + Parameters + ---------- + string : unicode + The string whose hash needs to be calculated. + + Returns + ------- + unsigned int + The hash of the string. + + """ + cdef unsigned int h = 2166136261 + for c in string: + h ^= ord(c) + h *= 16777619 + return h + + +cpdef compute_ngrams(word, unsigned int min_n, unsigned int max_n): + """Get the list of all possible ngrams for a given word. + + Parameters + ---------- + word : str + The word whose ngrams need to be computed. + min_n : unsigned int + Minimum character length of the ngrams. + max_n : unsigned int + Maximum character length of the ngrams. + + Returns + ------- + list of str + Sequence of character ngrams. + + """ + cdef unicode extended_word = f'<{word}>' + ngrams = [] + for ngram_length in range(min_n, min(len(extended_word), max_n) + 1): + for i in range(0, len(extended_word) - ngram_length + 1): + ngrams.append(extended_word[i:i + ngram_length]) + return ngrams + +# +# UTF-8 bytes that begin with 10 are subsequent bytes of a multi-byte sequence, +# as opposed to a new character. +# +cdef unsigned char _MB_MASK = 0xC0 +cdef unsigned char _MB_START = 0x80 + + +cpdef compute_ngrams_bytes(word, unsigned int min_n, unsigned int max_n): + """Computes ngrams for a word. + + Ported from the original FB implementation. + + Parameters + ---------- + word : str + A unicode string. + min_n : unsigned int + The minimum ngram length. + max_n : unsigned int + The maximum ngram length. + + Returns: + -------- + list of str + A list of ngrams, where each ngram is a list of **bytes**. + + See Also + -------- + `Original implementation `__ + + """ + cdef bytes utf8_word = ('<%s>' % word).encode("utf-8") + cdef const unsigned char *bytez = utf8_word + cdef size_t num_bytes = len(utf8_word) + cdef size_t j, i, n + + ngrams = [] + for i in range(num_bytes): + if bytez[i] & _MB_MASK == _MB_START: + continue + + j, n = i, 1 + while j < num_bytes and n <= max_n: + j += 1 + while j < num_bytes and (bytez[j] & _MB_MASK) == _MB_START: + j += 1 + if n >= min_n and not (n == 1 and (i == 0 or j == num_bytes)): + ngram = bytes(bytez[i:j]) + ngrams.append(ngram) + n += 1 + return ngrams + + def init(): """Precompute function `sigmoid(x) = 1 / (1 + exp(-x))`, for x values discretized into table EXP_TABLE. Also calculate log(sigmoid(x)) into LOG_TABLE. diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index ac5ad9dd4f..4229a58105 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # -# Author: Shiva Manne +# Author: Gensim Contributors # Copyright (C) 2018 RaRe Technologies s.r.o. # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html @@ -158,8 +158,6 @@ """ -from __future__ import division # py3 "true division" - from itertools import chain import logging from numbers import Integral @@ -171,23 +169,16 @@ from numpy import dot, float32 as REAL, memmap as np_memmap, \ double, array, zeros, vstack, sqrt, newaxis, integer, \ - ndarray, sum as np_sum, prod, argmax + ndarray, sum as np_sum, prod, argmax, dtype, ascontiguousarray, \ + frombuffer 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 import string_types, integer_types, iteritems from six.moves import zip, range from scipy import stats -from gensim.utils import deprecated -from gensim.models.utils_any2vec import ( - _save_word2vec_format, - _load_word2vec_format, - ft_ngram_hashes, -) -from gensim.similarities.termsim import TermSimilarityIndex, SparseTermSimilarityMatrix -# # For backwards compatibility, see https://github.com/RaRe-Technologies/gensim/issues/2201 # from gensim.models.deprecated.keyedvectors import EuclideanKeyedVectors # noqa @@ -195,85 +186,86 @@ 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). +class KeyedVectors(utils.SaveLoad): + """Abstract base class / interface for various types of word vectors.""" + """Class containing common methods for operations over word vectors.""" + """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 __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), dtype=REAL) + self.vectors = zeros((0, vector_size), dtype=REAL) # was syn0, once upon a time + self.vectors_norm = None # was syn0norm, once upon a time self.vocab = {} self.vector_size = vector_size - self.index2entity = [] - - def save(self, fname_or_handle, **kwargs): - super(BaseKeyedVectors, self).save(fname_or_handle, **kwargs) + self.index2key = [] # fka index2entity or index2word @classmethod def load(cls, fname_or_handle, **kwargs): - return super(BaseKeyedVectors, cls).load(fname_or_handle, **kwargs) + _kv = super(KeyedVectors, cls).load(fname_or_handle, **kwargs) + # handle rename/consolidation into index2key + return _kv - def similarity(self, entity1, entity2): - """Compute cosine similarity between two entities, specified by their string id.""" - raise NotImplementedError() + def _load_specials(self, *args, **kwargs): + super(KeyedVectors, self)._load_specials(*args, **kwargs) + # fixup rename/consolidation into index2key of older index2word, index2entity + if not hasattr(self, 'index2key'): + self.index2key = self.__dict__.pop('index2word', self.__dict__.pop('index2word', None)) - 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 __getitem__(self, entities): + """Get vector representation of `entities`. - def distance(self, entity1, entity2): - """Compute distance between vectors of two input entities, specified by their string id.""" - raise NotImplementedError() + Parameters + ---------- + entities : {str, list of str} + Input entity/entities. - 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. + Returns + ------- + numpy.ndarray + Vector representation for `entities` (1D if `entities` is string, otherwise - 2D). """ - raise NotImplementedError() + if isinstance(entities, string_types): + # allow calls like trained_model['office'], as a shorthand for trained_model[['office']] + return self.get_vector(entities) - def get_vector(self, entity): + return vstack([self.get_vector(entity) for entity in entities]) + + def get_vector(self, key, use_norm=False): """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. + key : str + Identifier of the vector to return + use_norm : bool, optional + If True - resulting vector will be L2-normalized (unit euclidean length). Returns ------- numpy.ndarray - Vector for the specified entity. + Vector for the specified key. Raises ------ KeyError - If the given entity identifier doesn't exist. + If the given key doesn't exist. """ - if entity in self.vocab: - result = self.vectors[self.vocab[entity].index] + if key in self.vocab: + if use_norm: + result = self.vectors_norm[self.vocab[key].index] + else: + result = self.vectors[self.vocab[key].index] result.setflags(write=False) return result else: - raise KeyError("'%s' not in vocabulary" % entity) + raise KeyError("Key '%s' not in vocabulary" % key) + + def word_vec(self, *args, **kwargs): + """Compatibility alias for get_vector()""" + return self.get_vector(*args, **kwargs) def add(self, entities, weights, replace=False): """Append entities and theirs vectors in a manual way. @@ -318,7 +310,7 @@ def add(self, entities, weights, replace=False): 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`. + This method is alias for :meth:`~gensim.models.keyedvectors.KeyedVectors.add` with `replace=True`. Parameters ---------- @@ -334,28 +326,8 @@ def __setitem__(self, entities, weights): 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 __contains__(self, key): + return key in self.vocab def most_similar_to_given(self, entity1, entities_list): """Get the `entity` from `entities_list` most similar to `entity1`.""" @@ -369,53 +341,29 @@ def closer_than(self, entity1, entity2): 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 words_closer_than(self, word1, word2): + return self.closer_than(word1, word2) + 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 - + # backward compatibility @property def index2entity(self): - return self.index2word + return self.index2key @index2entity.setter def index2entity(self, value): - self.index2word = value - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.vectors instead") - def syn0(self): - return self.vectors - - @syn0.setter - @deprecated("Attribute will be removed in 4.0.0, use self.vectors instead") - def syn0(self, value): - self.vectors = value + self.index2key = value @property - @deprecated("Attribute will be removed in 4.0.0, use self.vectors_norm instead") - def syn0norm(self): - return self.vectors_norm - - @syn0norm.setter - @deprecated("Attribute will be removed in 4.0.0, use self.vectors_norm instead") - def syn0norm(self, value): - self.vectors_norm = value + def index2word(self): + return self.index2key - def __contains__(self, word): - return word in self.vocab + @index2word.setter + def index2word(self, value): + self.index2key = value def save(self, *args, **kwargs): """Save KeyedVectors. @@ -427,66 +375,13 @@ def save(self, *args, **kwargs): See Also -------- - :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.load` + :meth:`~gensim.models.keyedvectors.KeyedVectors.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) + super(KeyedVectors, self).save(*args, **kwargs) def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=None, indexer=None): """Find the top-N most similar words. @@ -621,59 +516,6 @@ def similar_by_vector(self, vector, topn=10, restrict_vocab=None): """ return self.most_similar(positive=[vector], topn=topn, restrict_vocab=restrict_vocab) - @deprecated( - "Method will be removed in 4.0.0, use " - "gensim.models.keyedvectors.WordEmbeddingSimilarityIndex instead") - 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 the considered terms. - tfidf : :class:`gensim.models.tfidfmodel.TfidfModel` or None, optional - A model that specifies the relative importance of the terms in the dictionary. The - columns 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 embeddings more similar than `threshold` are considered when retrieving word - embeddings closest to a given word embedding. - exponent : float, optional - Take the word embedding similarities larger than `threshold` to the power of `exponent`. - nonzero_limit : int, optional - The maximum number of non-zero elements outside the diagonal in a single column of the - sparse term similarity matrix. - dtype : numpy.dtype, optional - Data-type of the sparse 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 - `_. - - """ - index = WordEmbeddingSimilarityIndex(self, threshold=threshold, exponent=exponent) - similarity_matrix = SparseTermSimilarityMatrix( - index, dictionary, tfidf=tfidf, nonzero_limit=nonzero_limit, dtype=dtype) - return similarity_matrix.matrix - def wmdistance(self, document1, document2): """Compute the Word Mover's Distance between two documents. @@ -788,7 +630,7 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): 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`. + :meth:`~gensim.models.keyedvectors.KeyedVectors.most_similar`. Parameters ---------- @@ -938,7 +780,7 @@ def distances(self, word_or_vector, other_words=()): def distance(self, w1, w2): """Compute cosine distance between two words. - Calculate 1 - :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similarity`. + Calculate 1 - :meth:`~gensim.models.keyedvectors.KeyedVectors.similarity`. Parameters ---------- @@ -998,7 +840,7 @@ def n_similarity(self, ws1, ws2): @staticmethod def _log_evaluate_word_analogies(section): """Calculate score by section, helper for - :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.evaluate_word_analogies`. + :meth:`~gensim.models.keyedvectors.KeyedVectors.evaluate_word_analogies`. Parameters ---------- @@ -1020,7 +862,7 @@ def _log_evaluate_word_analogies(section): 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 + This is modern variant of :meth:`~gensim.models.keyedvectors.KeyedVectors.accuracy`, see `discussion on GitHub #1935 `_. The accuracy is reported (printed to log and returned as a score) for each section separately, @@ -1142,95 +984,6 @@ def log_accuracy(section): 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 - with utils.open(questions, 'rb') as fin: - for line_no, line in enumerate(fin): - # 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=None, 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': list(chain.from_iterable(s['correct'] for s in sections)), - 'incorrect': list(chain.from_iterable(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]) @@ -1345,8 +1098,8 @@ def init_sims(self, replace=False): -------- 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. + :meth:`~gensim.models.keyedvectors.KeyedVectors.most_similar`, + :meth:`~gensim.models.keyedvectors.KeyedVectors.similarity`, etc., but not train. """ if getattr(self, 'vectors_norm', None) is None or replace: @@ -1383,105 +1136,6 @@ def relative_cosine_similarity(self, wa, wb, topn=10): return rcs - def get_keras_embedding(self, train_embeddings=False, word_index=None): - """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. - - word_index : {str : int} - A mapping from tokens to their indices the way they will be provided in the input to the embedding layer. - The embedding of each token will be placed at the corresponding index in the returned matrix. - Tokens not in the index are ignored. - This is useful when the token indices are produced by a process that is not coupled with the embedding - model, e.x. an Keras Tokenizer object. - If None, the embedding matrix in the embedding layer will be indexed according to self.vocab - - Returns - ------- - `keras.layers.Embedding` - Embedding layer. - - Raises - ------ - ImportError - If `Keras `_ not installed. - - Warnings - -------- - Current method works only if `Keras `_ installed. - - """ - try: - from keras.layers import Embedding - except ImportError: - raise ImportError("Please install Keras to use this function") - if word_index is None: - weights = self.vectors - else: - max_index = max(word_index.values()) - weights = np.random.normal(size=(max_index + 1, self.vectors.shape[1])) - for word, index in word_index.items(): - if word in self.vocab: - weights[index] = self.get_vector(word) - - layer = Embedding( - input_dim=weights.shape[0], output_dim=weights.shape[1], - weights=[weights], trainable=train_embeddings - ) - return layer - - -class WordEmbeddingSimilarityIndex(TermSimilarityIndex): - """ - Computes cosine similarities between word embeddings and retrieves the closest word embeddings - by cosine similarity for a given word embedding. - - Parameters - ---------- - keyedvectors : :class:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors` - The word embeddings. - threshold : float, optional - Only embeddings more similar than `threshold` are considered when retrieving word embeddings - closest to a given word embedding. - exponent : float, optional - Take the word embedding similarities larger than `threshold` to the power of `exponent`. - kwargs : dict or None - A dict with keyword arguments that will be passed to the `keyedvectors.most_similar` method - when retrieving the word embeddings closest to a given word embedding. - - See Also - -------- - :class:`~gensim.similarities.termsim.SparseTermSimilarityMatrix` - Build a term similarity matrix and compute the Soft Cosine Measure. - - """ - def __init__(self, keyedvectors, threshold=0.0, exponent=2.0, kwargs=None): - assert isinstance(keyedvectors, WordEmbeddingsKeyedVectors) - self.keyedvectors = keyedvectors - self.threshold = threshold - self.exponent = exponent - self.kwargs = kwargs or {} - super(WordEmbeddingSimilarityIndex, self).__init__() - - def most_similar(self, t1, topn=10): - if t1 not in self.keyedvectors.vocab: - logger.debug('an out-of-dictionary term "%s"', t1) - else: - most_similar = self.keyedvectors.most_similar(positive=[t1], topn=topn, **self.kwargs) - for t2, similarity in most_similar: - if similarity > self.threshold: - yield (t2, similarity**self.exponent) - - -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. @@ -1499,7 +1153,6 @@ def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None) (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) @@ -1539,29 +1192,58 @@ def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', Returns ------- - :class:`~gensim.models.keyedvectors.Word2VecKeyedVectors` + :class:`~gensim.models.keyedvectors.KeyedVectors` 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) - @classmethod - def load(cls, fname_or_handle, **kwargs): - model = super(WordEmbeddingsKeyedVectors, cls).load(fname_or_handle, **kwargs) - if isinstance(model, FastTextKeyedVectors): - if not hasattr(model, 'compatible_hash'): - model.compatible_hash = False + def get_keras_embedding(self, train_embeddings=False): + """Get a Keras 'Embedding' layer with weights set as the Word2Vec model's learned word embeddings. - return model + 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. -KeyedVectors = Word2VecKeyedVectors # alias for backward compatibility + Raises + ------ + ImportError + If `Keras `_ not installed. + Warnings + -------- + Current method work only if `Keras `_ installed. -class Doc2VecKeyedVectors(BaseKeyedVectors): + """ + 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 + + +# to help 3.8.1 & older pickles load properly +Word2VecKeyedVectors = KeyedVectors + + +class Doc2VecKeyedVectors(KeyedVectors): def __init__(self, vector_size, mapfile_path): super(Doc2VecKeyedVectors, self).__init__(vector_size=vector_size) @@ -1582,16 +1264,6 @@ def index2entity(self): 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`. @@ -1953,419 +1625,6 @@ def int_index(self, index, doctags, max_rawint): return max_rawint + 1 + doctags[index].offset -class FastTextKeyedVectors(WordEmbeddingsKeyedVectors): - """Vectors and vocab for :class:`~gensim.models.fasttext.FastText`. - - Implements significant parts of the FastText algorithm. For example, - the :func:`word_vec` calculates vectors for out-of-vocabulary (OOV) - entities. FastText achieves this by keeping vectors for ngrams: - adding the vectors for the ngrams of an entity yields the vector for the - entity. - - Similar to a hashmap, this class keeps a fixed number of buckets, and - maps all ngrams to buckets using a hash function. - - This class also provides an abstraction over the hash functions used by - Gensim's FastText implementation over time. The hash function connects - ngrams to buckets. Originally, the hash function was broken and - incompatible with Facebook's implementation. The current hash is fully - compatible. - - Parameters - ---------- - vector_size : int - The dimensionality of all vectors. - min_n : int - The minimum number of characters in an ngram - max_n : int - The maximum number of characters in an ngram - bucket : int - The number of buckets. - compatible_hash : boolean - If True, uses the Facebook-compatible hash function instead of the - Gensim backwards-compatible hash function. - - Attributes - ---------- - vectors_vocab : np.array - Each row corresponds to a vector for an entity in the vocabulary. - Columns correspond to vector dimensions. - vectors_vocab_norm : np.array - Same as vectors_vocab, but the vectors are L2 normalized. - vectors_ngrams : np.array - A vector for each ngram across all entities in the vocabulary. - Each row is a vector that corresponds to a bucket. - Columns correspond to vector dimensions. - vectors_ngrams_norm : np.array - Same as vectors_ngrams, but the vectors are L2 normalized. - Under some conditions, may actually be the same matrix as - vectors_ngrams, e.g. if :func:`init_sims` was called with - replace=True. - buckets_word : dict - Maps vocabulary items (by their index) to the buckets they occur in. - - """ - def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): - 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.min_n = min_n - self.max_n = max_n - self.bucket = bucket - self.compatible_hash = compatible_hash - - @classmethod - def load(cls, fname_or_handle, **kwargs): - model = super(WordEmbeddingsKeyedVectors, cls).load(fname_or_handle, **kwargs) - _try_upgrade(model) - return model - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.vectors_vocab instead") - def syn0_vocab(self): - return self.vectors_vocab - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.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.vectors_ngrams instead") - def syn0_ngrams(self): - return self.vectors_ngrams - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.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. - - Note - ---- - This method **always** returns True, because of the way FastText works. - - If you want to check if a word is an in-vocabulary term, use this instead: - - .. pycon: - - >>> from gensim.test.utils import datapath - >>> from gensim.models import FastText - >>> cap_path = datapath("crime-and-punishment.bin") - >>> model = FastText.load_fasttext_format(cap_path, full_model=False) - >>> 'steamtrain' in model.wv.vocab # If False, is an OOV term - False - - """ - return True - - 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 - ignore_attrs = [ - 'vectors_norm', - 'vectors_vocab_norm', - 'vectors_ngrams_norm', - 'buckets_word', - 'hash2index', - ] - kwargs['ignore'] = kwargs.get('ignore', ignore_attrs) - 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) - elif self.bucket == 0: - raise KeyError('cannot calculate vector for OOV word without ngrams') - else: - word_vec = np.zeros(self.vectors_ngrams.shape[1], dtype=REAL) - ngram_hashes = ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash) - if len(ngram_hashes) == 0: - # - # If it is impossible to extract _any_ ngrams from the input - # word, then the best we can do is return a vector that points - # to the origin. The reference FB implementation does this, - # too. - # - # https://github.com/RaRe-Technologies/gensim/issues/2402 - # - logger.warning('could not extract any ngrams from %r, returning origin vector', word) - return word_vec - for nh in ngram_hashes: - word_vec += self.vectors_ngrams[nh] - result = word_vec / len(ngram_hashes) - if use_norm: - result /= sqrt(sum(result ** 2)) - return result - - 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") - self.vectors_ngrams_norm = _l2_norm(self.vectors_ngrams, replace=replace) - - 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) - - def init_ngrams_weights(self, seed): - """Initialize the vocabulary and ngrams weights prior to training. - - Creates the weight matrices and initializes them with uniform random values. - - Parameters - ---------- - seed : float - The seed for the PRNG. - - Note - ---- - Call this **after** the vocabulary has been fully initialized. - - """ - self.buckets_word = _process_fasttext_vocab( - self.vocab.items(), - self.min_n, - self.max_n, - self.bucket, - self.compatible_hash, - ) - - rand_obj = np.random - rand_obj.seed(seed) - - lo, hi = -1.0 / self.vector_size, 1.0 / self.vector_size - vocab_shape = (len(self.vocab), self.vector_size) - ngrams_shape = (self.bucket, self.vector_size) - self.vectors_vocab = rand_obj.uniform(lo, hi, vocab_shape).astype(REAL) - - # - # We could have initialized vectors_ngrams at construction time, but we - # do it here for two reasons: - # - # 1. The constructor does not have access to the random seed - # 2. We want to use the same rand_obj to fill vectors_vocab _and_ - # vectors_ngrams, and vectors_vocab cannot happen at construction - # time because the vocab is not initialized at that stage. - # - self.vectors_ngrams = rand_obj.uniform(lo, hi, ngrams_shape).astype(REAL) - - def update_ngrams_weights(self, seed, old_vocab_len): - """Update the vocabulary weights for training continuation. - - Parameters - ---------- - seed : float - The seed for the PRNG. - old_vocab_length : int - The length of the vocabulary prior to its update. - - Note - ---- - Call this **after** the vocabulary has been updated. - - """ - self.buckets_word = _process_fasttext_vocab( - self.vocab.items(), - self.min_n, - self.max_n, - self.bucket, - self.compatible_hash, - ) - - rand_obj = np.random - rand_obj.seed(seed) - - new_vocab = len(self.vocab) - old_vocab_len - self.vectors_vocab = _pad_random(self.vectors_vocab, new_vocab, rand_obj) - - def init_post_load(self, vectors): - """Perform initialization after loading a native Facebook model. - - Expects that the vocabulary (self.vocab) has already been initialized. - - Parameters - ---------- - vectors : np.array - A matrix containing vectors for all the entities, including words - and ngrams. This comes directly from the binary model. - The order of the vectors must correspond to the indices in - the vocabulary. - match_gensim : boolean, optional - No longer supported. - - """ - vocab_words = len(self.vocab) - assert vectors.shape[0] == vocab_words + self.bucket, 'unexpected number of vectors' - assert vectors.shape[1] == self.vector_size, 'unexpected vector dimensionality' - - # - # The incoming vectors contain vectors for both words AND - # ngrams. We split them into two separate matrices, because our - # implementation treats them differently. - # - self.vectors = np.array(vectors[:vocab_words, :]) - self.vectors_vocab = np.array(vectors[:vocab_words, :]) - self.vectors_ngrams = np.array(vectors[vocab_words:, :]) - self.buckets_word = None # This can get initialized later - - self.adjust_vectors() - - def adjust_vectors(self): - """Adjust the vectors for words in the vocabulary. - - The adjustment relies on the vectors of the ngrams making up each - individual word. - - """ - if self.bucket == 0: - return - - for w, v in self.vocab.items(): - word_vec = np.copy(self.vectors_vocab[v.index]) - ngram_hashes = ft_ngram_hashes(w, self.min_n, self.max_n, self.bucket, self.compatible_hash) - for nh in ngram_hashes: - word_vec += self.vectors_ngrams[nh] - word_vec /= len(ngram_hashes) + 1 - self.vectors[v.index] = word_vec - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.bucket instead") - def num_ngram_vectors(self): - return self.bucket - - -def _process_fasttext_vocab(iterable, min_n, max_n, num_buckets, compatible_hash): - """ - Performs a common operation for FastText weight initialization and - updates: scan the vocabulary, calculate ngrams and their hashes, keep - track of new ngrams, the buckets that each word relates to via its - ngrams, etc. - - Parameters - ---------- - iterable : list - A list of (word, :class:`Vocab`) tuples. - min_n : int - The minimum length of ngrams. - max_n : int - The maximum length of ngrams. - num_buckets : int - The number of buckets used by the model. - compatible_hash : boolean - True for compatibility with the Facebook implementation. - False for compatibility with the old Gensim implementation. - - Returns - ------- - dict - Keys are indices of entities in the vocabulary (words). Values are - arrays containing indices into vectors_ngrams for each ngram of the - word. - - """ - word_indices = {} - - if num_buckets == 0: - return {v.index: np.array([], dtype=np.uint32) for w, v in iterable} - - for word, vocab in iterable: - wi = [] - for ngram_hash in ft_ngram_hashes(word, min_n, max_n, num_buckets, compatible_hash): - wi.append(ngram_hash) - word_indices[vocab.index] = np.array(wi, dtype=np.uint32) - - return word_indices - - -def _pad_random(m, new_rows, rand): - """Pad a matrix with additional rows filled with random values.""" - rows, columns = m.shape - low, high = -1.0 / columns, 1.0 / columns - suffix = rand.uniform(low, high, (new_rows, columns)).astype(REAL) - return vstack([m, suffix]) - - def _l2_norm(m, replace=False): """Return an L2-normalized version of a matrix. @@ -2389,138 +1648,214 @@ def _l2_norm(m, replace=False): return (m / dist).astype(REAL) -def _rollback_optimization(kv): - """Undo the optimization that pruned buckets. - - This unfortunate optimization saves memory and CPU cycles, but breaks - compatibility with Facebook's model by introducing divergent behavior - for OOV words. +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). """ - logger.warning( - "This saved FastText model was trained with an optimization we no longer support. " - "The current Gensim version automatically reverses this optimization during loading. " - "Save the loaded model to a new file and reload to suppress this message." - ) - assert hasattr(kv, 'hash2index') - assert hasattr(kv, 'num_ngram_vectors') + def __init__(self, **kwargs): + self.count = 0 + self.__dict__.update(kwargs) - kv.vectors_ngrams = _unpack(kv.vectors_ngrams, kv.bucket, kv.hash2index) + def __lt__(self, other): # used for sorting in a priority queue + return self.count < other.count - # - # We have replaced num_ngram_vectors with a property and deprecated it. - # We can't delete it because the new attribute masks the member. - # - del kv.hash2index + 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)) -def _unpack_copy(m, num_rows, hash2index, seed=1): - """Same as _unpack, but makes a copy of the matrix. +def _save_word2vec_format(fname, vocab, vectors, 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. - Simpler implementation, but uses more RAM. + Parameters + ---------- + fname : str + The file path used to save the vectors in. + vocab : dict + The vocabulary of words. + vectors : numpy.array + The vectors to be stored. + fvocab : str, 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 + Explicitly specify total number of vectors + (in case word vectors are appended with document vectors afterwards). """ - rows, columns = m.shape - if rows == num_rows: - # - # Nothing to do. - # - return m - assert num_rows > rows - - rand_obj = np.random - rand_obj.seed(seed) - - n = np.empty((0, columns), dtype=m.dtype) - n = _pad_random(n, num_rows, rand_obj) - - for src, dst in hash2index.items(): - n[src] = m[dst] + if not (vocab or vectors): + raise RuntimeError("no input") + if total_vec is None: + total_vec = len(vocab) + vector_size = vectors.shape[1] + if fvocab is not None: + logger.info("storing vocabulary in %s", fvocab) + with utils.open(fvocab, 'wb') as vout: + for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count): + vout.write(utils.to_utf8("%s %s\n" % (word, vocab_.count))) + logger.info("storing %sx%s projection weights into %s", total_vec, vector_size, fname) + assert (len(vocab), vector_size) == vectors.shape + with utils.open(fname, 'wb') as fout: + fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size))) + # store in sorted order: most frequent words at the top + for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count): + row = vectors[vocab_.index] + if binary: + row = row.astype(REAL) + fout.write(utils.to_utf8(word) + b" " + row.tostring()) + else: + fout.write(utils.to_utf8("%s %s\n" % (word, ' '.join(repr(val) for val in row)))) - return n +# Functions for internal use by _load_word2vec_format function -def _unpack(m, num_rows, hash2index, seed=1): - """Restore the array to its natural shape, undoing the optimization. - A packed matrix contains contiguous vectors for ngrams, as well as a hashmap. - The hash map maps the ngram hash to its index in the packed matrix. - To unpack the matrix, we need to do several things: +def _add_word_to_result(result, counts, word, weights, vocab_size): - 1. Restore the matrix to its "natural" shape, where the number of rows - equals the number of buckets. - 2. Rearrange the existing rows such that the hashmap becomes the identity - function and is thus redundant. - 3. Fill the new rows with random values. + word_id = len(result.vocab) + if word in result.vocab: + logger.warning("duplicate word '%s' in word2vec file, ignoring all but first", word) + return + if counts is None: + # most common scenario: no vocab file given. just make up some bogus counts, in descending order + word_count = vocab_size - word_id + elif word in counts: + # use count from the vocab file + word_count = counts[word] + else: + logger.warning("vocabulary file is incomplete: '%s' is missing", word) + word_count = None + + result.vocab[word] = Vocab(index=word_id, count=word_count) + result.vectors[word_id] = weights + result.index2word.append(word) + + +def _add_bytes_to_result(result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors): + start = 0 + processed_words = 0 + bytes_per_vector = vector_size * dtype(REAL).itemsize + max_words = vocab_size - len(result.vocab) + for _ in range(max_words): + i_space = chunk.find(b' ', start) + i_vector = i_space + 1 + + if i_space == -1 or (len(chunk) - i_vector) < bytes_per_vector: + break + + word = chunk[start:i_space].decode("utf-8", errors=unicode_errors) + # Some binary files are reported to have obsolete new line in the beginning of word, remove it + word = word.lstrip('\n') + vector = frombuffer(chunk, offset=i_vector, count=vector_size, dtype=REAL).astype(datatype) + _add_word_to_result(result, counts, word, vector, vocab_size) + start = i_vector + bytes_per_vector + processed_words += 1 + + return processed_words, chunk[start:] + + +def _word2vec_read_binary(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size): + chunk = b'' + tot_processed_words = 0 + + while tot_processed_words < vocab_size: + new_chunk = fin.read(binary_chunk_size) + chunk += new_chunk + processed_words, chunk = _add_bytes_to_result( + result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors) + tot_processed_words += processed_words + if len(new_chunk) < binary_chunk_size: + break + if tot_processed_words != vocab_size: + raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") + + +def _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, encoding): + for line_no in range(vocab_size): + line = fin.readline() + if line == b'': + raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") + parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") + if len(parts) != vector_size + 1: + raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) + word, weights = parts[0], [datatype(x) for x in parts[1:]] + _add_word_to_result(result, counts, word, weights, vocab_size) + + +def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', + limit=None, datatype=REAL, binary_chunk_size=100 * 1024): + """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. Parameters ---------- - - m : np.ndarray - The matrix to restore. - num_rows : int - The number of rows that this array should have. - hash2index : dict - the product of the optimization we are undoing. - seed : float, optional - The seed for the PRNG. Will be used to initialize new rows. + 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.) + binary_chunk_size : int, optional + Read input file in chunks of this many bytes for performance reasons. Returns ------- - np.array - The unpacked matrix. - - Notes - ----- - - The unpacked matrix will reference some rows in the input matrix to save memory. - Throw away the old matrix after calling this function, or use np.copy. + object + Returns the loaded model as an instance of :class:`cls`. """ - orig_rows, orig_columns = m.shape - if orig_rows == num_rows: - # - # Nothing to do. - # - return m - assert num_rows > orig_rows - - rand_obj = np.random - rand_obj.seed(seed) - - # - # Rows at the top of the matrix (the first orig_rows) will contain "packed" learned vectors. - # Rows at the bottom of the matrix will be "free": initialized to random values. - # - m = _pad_random(m, num_rows - orig_rows, rand_obj) - - # - # Swap rows to transform hash2index into the identify function. - # There are two kinds of swaps. - # First, rearrange the rows that belong entirely within the original matrix dimensions. - # Second, swap out rows from the original matrix dimensions, replacing them with - # randomly initialized values. - # - # N.B. We only do the swap in one direction, because doing it in both directions - # nullifies the effect. - # - swap = {h: i for (h, i) in hash2index.items() if h < i < orig_rows} - swap.update({h: i for (h, i) in hash2index.items() if h >= orig_rows}) - for h, i in swap.items(): - assert h != i - m[[h, i]] = m[[i, h]] # swap rows i and h - - return m - - -def _try_upgrade(wv): - if hasattr(wv, 'hash2index'): - _rollback_optimization(wv) - - if not hasattr(wv, 'compatible_hash'): - logger.warning( - "This older model was trained with a buggy hash function. " - "The model will continue to work, but consider training it " - "from scratch." + + counts = None + if fvocab is not None: + logger.info("loading word counts from %s", fvocab) + counts = {} + with utils.open(fvocab, 'rb') as fin: + for line in fin: + word, count = utils.to_unicode(line, errors=unicode_errors).strip().split() + counts[word] = int(count) + + logger.info("loading projection weights from %s", fname) + with utils.open(fname, 'rb') as fin: + header = utils.to_unicode(fin.readline(), encoding=encoding) + vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format + if limit: + vocab_size = min(vocab_size, limit) + result = cls(vector_size) + result.vector_size = vector_size + result.vectors = zeros((vocab_size, vector_size), dtype=datatype) + + if binary: + _word2vec_read_binary(fin, result, counts, + vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size) + else: + _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, encoding) + if result.vectors.shape[0] != len(result.vocab): + logger.info( + "duplicate words detected, shrinking matrix size from %i to %i", + result.vectors.shape[0], len(result.vocab) ) - wv.compatible_hash = False + result.vectors = ascontiguousarray(result.vectors[: len(result.vocab)]) + assert (len(result.vocab), vector_size) == result.vectors.shape + + logger.info("loaded %s matrix from %s", result.vectors.shape, fname) + return result diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index a5c4539e34..f161073f1a 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -56,8 +56,7 @@ from six.moves import zip, range from gensim import utils, matutils -from gensim.models.keyedvectors import Vocab, BaseKeyedVectors -from gensim.models.utils_any2vec import _save_word2vec_format, _load_word2vec_format +from gensim.models.keyedvectors import Vocab, KeyedVectors, _save_word2vec_format, _load_word2vec_format from numpy import float32 as REAL try: @@ -860,7 +859,7 @@ def compute_loss(self): self._loss_computed = True -class PoincareKeyedVectors(BaseKeyedVectors): +class PoincareKeyedVectors(KeyedVectors): """Vectors and vocab for the :class:`~gensim.models.poincare.PoincareModel` training class. Used to perform operations on the vectors such as vector lookup, distance calculations etc. diff --git a/gensim/models/utils_any2vec.py b/gensim/models/utils_any2vec.py deleted file mode 100644 index afc25c772b..0000000000 --- a/gensim/models/utils_any2vec.py +++ /dev/null @@ -1,298 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Author: Shiva Manne -# Copyright (C) 2019 RaRe Technologies s.r.o. - -"""General functions used for any2vec models. - -One of the goals of this module is to provide an abstraction over the Cython -extensions for FastText. If they are not available, then the module substitutes -slower Python versions in their place. - -Another related set of FastText functionality is computing ngrams for a word. -The :py:func:`compute_ngrams` and :py:func:`compute_ngrams_bytes` hashes achieve that. - -Closely related is the functionality for hashing ngrams, implemented by the -:py:func:`ft_hash` and :py:func:`ft_hash_broken` functions. -The module exposes "working" and "broken" hash functions in order to maintain -backwards compatibility with older versions of Gensim. - -For compatibility with older Gensim, use :py:func:`compute_ngrams` and -:py:func:`ft_hash_broken` to has each ngram. For compatibility with the -current Facebook implementation, use :py:func:`compute_ngrams_bytes` and -:py:func:`ft_hash_bytes`. - -""" - -import logging -from gensim import utils -import gensim.models.keyedvectors - -from numpy import zeros, dtype, float32 as REAL, ascontiguousarray, frombuffer - -from six.moves import range -from six import iteritems, PY2 - -logger = logging.getLogger(__name__) - - -# -# UTF-8 bytes that begin with 10 are subsequent bytes of a multi-byte sequence, -# as opposed to a new character. -# -_MB_MASK = 0xC0 -_MB_START = 0x80 - - -def _byte_to_int_py3(b): - return b - - -def _byte_to_int_py2(b): - return ord(b) - - -_byte_to_int = _byte_to_int_py2 if PY2 else _byte_to_int_py3 - - -def _is_utf8_continue(b): - return _byte_to_int(b) & _MB_MASK == _MB_START - - -try: - from gensim.models._utils_any2vec import ( - compute_ngrams, - compute_ngrams_bytes, - ft_hash_broken, - ft_hash_bytes, - ) -except ImportError: - raise utils.NO_CYTHON - - -def ft_ngram_hashes(word, minn, maxn, num_buckets, fb_compatible=True): - """Calculate the ngrams of the word and hash them. - - Parameters - ---------- - word : str - The word to calculate ngram hashes for. - minn : int - Minimum ngram length - maxn : int - Maximum ngram length - num_buckets : int - The number of buckets - fb_compatible : boolean, optional - True for compatibility with the Facebook implementation. - False for compatibility with the old Gensim implementation. - - Returns - ------- - A list of hashes (integers), one per each detected ngram. - - """ - if fb_compatible: - encoded_ngrams = compute_ngrams_bytes(word, minn, maxn) - hashes = [ft_hash_bytes(n) % num_buckets for n in encoded_ngrams] - else: - text_ngrams = compute_ngrams(word, minn, maxn) - hashes = [ft_hash_broken(n) % num_buckets for n in text_ngrams] - return hashes - - -def _save_word2vec_format(fname, vocab, vectors, 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. - vocab : dict - The vocabulary of words. - vectors : numpy.array - The vectors to be stored. - fvocab : str, 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 - Explicitly specify total number of vectors - (in case word vectors are appended with document vectors afterwards). - - """ - if not (vocab or vectors): - raise RuntimeError("no input") - if total_vec is None: - total_vec = len(vocab) - vector_size = vectors.shape[1] - if fvocab is not None: - logger.info("storing vocabulary in %s", fvocab) - with utils.open(fvocab, 'wb') as vout: - for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count): - vout.write(utils.to_utf8("%s %s\n" % (word, vocab_.count))) - logger.info("storing %sx%s projection weights into %s", total_vec, vector_size, fname) - assert (len(vocab), vector_size) == vectors.shape - with utils.open(fname, 'wb') as fout: - fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size))) - # store in sorted order: most frequent words at the top - for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count): - row = vectors[vocab_.index] - if binary: - row = row.astype(REAL) - fout.write(utils.to_utf8(word) + b" " + row.tostring()) - else: - fout.write(utils.to_utf8("%s %s\n" % (word, ' '.join(repr(val) for val in row)))) - - -# Functions for internal use by _load_word2vec_format function - - -def _add_word_to_result(result, counts, word, weights, vocab_size): - - word_id = len(result.vocab) - if word in result.vocab: - logger.warning("duplicate word '%s' in word2vec file, ignoring all but first", word) - return - if counts is None: - # most common scenario: no vocab file given. just make up some bogus counts, in descending order - word_count = vocab_size - word_id - elif word in counts: - # use count from the vocab file - word_count = counts[word] - else: - logger.warning("vocabulary file is incomplete: '%s' is missing", word) - word_count = None - - result.vocab[word] = gensim.models.keyedvectors.Vocab(index=word_id, count=word_count) - result.vectors[word_id] = weights - result.index2word.append(word) - - -def _add_bytes_to_result(result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors): - start = 0 - processed_words = 0 - bytes_per_vector = vector_size * dtype(REAL).itemsize - max_words = vocab_size - len(result.vocab) - for _ in range(max_words): - i_space = chunk.find(b' ', start) - i_vector = i_space + 1 - - if i_space == -1 or (len(chunk) - i_vector) < bytes_per_vector: - break - - word = chunk[start:i_space].decode("utf-8", errors=unicode_errors) - # Some binary files are reported to have obsolete new line in the beginning of word, remove it - word = word.lstrip('\n') - vector = frombuffer(chunk, offset=i_vector, count=vector_size, dtype=REAL).astype(datatype) - _add_word_to_result(result, counts, word, vector, vocab_size) - start = i_vector + bytes_per_vector - processed_words += 1 - - return processed_words, chunk[start:] - - -def _word2vec_read_binary(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size): - chunk = b'' - tot_processed_words = 0 - - while tot_processed_words < vocab_size: - new_chunk = fin.read(binary_chunk_size) - chunk += new_chunk - processed_words, chunk = _add_bytes_to_result( - result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors) - tot_processed_words += processed_words - if len(new_chunk) < binary_chunk_size: - break - if tot_processed_words != vocab_size: - raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") - - -def _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, encoding): - for line_no in range(vocab_size): - line = fin.readline() - if line == b'': - raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") - parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") - if len(parts) != vector_size + 1: - raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) - word, weights = parts[0], [datatype(x) for x in parts[1:]] - _add_word_to_result(result, counts, word, weights, vocab_size) - - -def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', - limit=None, datatype=REAL, binary_chunk_size=100 * 1024): - """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. - - 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.) - binary_chunk_size : int, optional - Read input file in chunks of this many bytes for performance reasons. - - Returns - ------- - object - Returns the loaded model as an instance of :class:`cls`. - - """ - - counts = None - if fvocab is not None: - logger.info("loading word counts from %s", fvocab) - counts = {} - with utils.open(fvocab, 'rb') as fin: - for line in fin: - word, count = utils.to_unicode(line, errors=unicode_errors).strip().split() - counts[word] = int(count) - - logger.info("loading projection weights from %s", fname) - with utils.open(fname, 'rb') as fin: - header = utils.to_unicode(fin.readline(), encoding=encoding) - vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format - if limit: - vocab_size = min(vocab_size, limit) - result = cls(vector_size) - result.vector_size = vector_size - result.vectors = zeros((vocab_size, vector_size), dtype=datatype) - - if binary: - _word2vec_read_binary(fin, result, counts, - vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size) - else: - _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, encoding) - if result.vectors.shape[0] != len(result.vocab): - logger.info( - "duplicate words detected, shrinking matrix size from %i to %i", - result.vectors.shape[0], len(result.vocab) - ) - result.vectors = ascontiguousarray(result.vectors[: len(result.vocab)]) - assert (len(result.vocab), vector_size) == result.vectors.shape - - logger.info("loaded %s matrix from %s", result.vectors.shape, fname) - return result diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 3c4c32efa5..eea4fcd86c 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -132,7 +132,7 @@ import warnings from gensim.utils import keep_vocab_item, call_on_class_only -from gensim.models.keyedvectors import Vocab, Word2VecKeyedVectors +from gensim.models.keyedvectors import Vocab, KeyedVectors from gensim.models.base_any2vec import BaseWordEmbeddingsModel try: @@ -457,7 +457,7 @@ class Word2Vec(BaseWordEmbeddingsModel): Attributes ---------- - wv : :class:`~gensim.models.keyedvectors.Word2VecKeyedVectors` + wv : :class:`~gensim.models.keyedvectors.KeyedVectors` This object essentially contains the mapping between words and embeddings. After training, it can be used directly to query those embeddings in various ways. See the module level docstring for examples. @@ -588,7 +588,7 @@ def __init__(self, sentences=None, corpus_file=None, size=100, alpha=0.025, wind self.callbacks = callbacks self.load = call_on_class_only - self.wv = Word2VecKeyedVectors(size) + self.wv = KeyedVectors(size) self.vocabulary = Word2VecVocab( max_vocab_size=max_vocab_size, min_count=min_count, sample=sample, sorted_vocab=bool(sorted_vocab), null_word=null_word, max_final_vocab=max_final_vocab, ns_exponent=ns_exponent) @@ -926,7 +926,7 @@ def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='ut @deprecated("Method will be removed in 4.0.0, use self.wv.__getitem__() instead") def __getitem__(self, words): """Deprecated. Use `self.wv.__getitem__` instead. - Refer to the documentation for :meth:`~gensim.models.keyedvectors.Word2VecKeyedVectors.__getitem__`. + Refer to the documentation for :meth:`~gensim.models.keyedvectors.KeyedVectors.__getitem__`. """ return self.wv.__getitem__(words) @@ -934,7 +934,7 @@ def __getitem__(self, words): @deprecated("Method will be removed in 4.0.0, use self.wv.__contains__() instead") def __contains__(self, word): """Deprecated. Use `self.wv.__contains__` instead. - Refer to the documentation for :meth:`~gensim.models.keyedvectors.Word2VecKeyedVectors.__contains__`. + Refer to the documentation for :meth:`~gensim.models.keyedvectors.KeyedVectors.__contains__`. """ return self.wv.__contains__(word) @@ -984,7 +984,7 @@ def predict_output_word(self, context_words_list, topn=10): def init_sims(self, replace=False): """Deprecated. Use `self.wv.init_sims` instead. - See :meth:`~gensim.models.keyedvectors.Word2VecKeyedVectors.init_sims`. + See :meth:`~gensim.models.keyedvectors.KeyedVectors.init_sims`. """ if replace and hasattr(self.trainables, 'syn1'): @@ -1017,18 +1017,18 @@ def reset_from(self, other_model): @staticmethod def log_accuracy(section): """Deprecated. Use `self.wv.log_accuracy` instead. - See :meth:`~gensim.models.word2vec.Word2VecKeyedVectors.log_accuracy`. + See :meth:`~gensim.models.word2vec.KeyedVectors.log_accuracy`. """ - return Word2VecKeyedVectors.log_accuracy(section) + return KeyedVectors.log_accuracy(section) @deprecated("Method will be removed in 4.0.0, use self.wv.evaluate_word_analogies() instead") def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_insensitive=True): """Deprecated. Use `self.wv.accuracy` instead. - See :meth:`~gensim.models.word2vec.Word2VecKeyedVectors.accuracy`. + See :meth:`~gensim.models.word2vec.KeyedVectors.accuracy`. """ - most_similar = most_similar or Word2VecKeyedVectors.most_similar + most_similar = most_similar or KeyedVectors.most_similar return self.wv.accuracy(questions, restrict_vocab, most_similar, case_insensitive) def __str__(self): diff --git a/gensim/similarities/__init__.py b/gensim/similarities/__init__.py index 3c670ba95b..3ab45261ad 100644 --- a/gensim/similarities/__init__.py +++ b/gensim/similarities/__init__.py @@ -13,5 +13,6 @@ from .termsim import ( # noqa:F401 TermSimilarityIndex, UniformTermSimilarityIndex, + WordEmbeddingSimilarityIndex, SparseTermSimilarityMatrix) from .levenshtein import LevenshteinSimilarityIndex # noqa:F401 diff --git a/gensim/similarities/termsim.py b/gensim/similarities/termsim.py index c4999fed37..51dcb6971f 100644 --- a/gensim/similarities/termsim.py +++ b/gensim/similarities/termsim.py @@ -113,6 +113,47 @@ def _shortest_uint_dtype(max_value): return np.uint64 +class WordEmbeddingSimilarityIndex(TermSimilarityIndex): + """ + Computes cosine similarities between word embeddings and retrieves the closest word embeddings + by cosine similarity for a given word embedding. + + Parameters + ---------- + keyedvectors : :class:`~gensim.models.keyedvectors.KeyedVectors` + The word embeddings. + threshold : float, optional + Only embeddings more similar than `threshold` are considered when retrieving word embeddings + closest to a given word embedding. + exponent : float, optional + Take the word embedding similarities larger than `threshold` to the power of `exponent`. + kwargs : dict or None + A dict with keyword arguments that will be passed to the `keyedvectors.most_similar` method + when retrieving the word embeddings closest to a given word embedding. + + See Also + -------- + :class:`~gensim.similarities.termsim.SparseTermSimilarityMatrix` + Build a term similarity matrix and compute the Soft Cosine Measure. + + """ + def __init__(self, keyedvectors, threshold=0.0, exponent=2.0, kwargs=None): + self.keyedvectors = keyedvectors + self.threshold = threshold + self.exponent = exponent + self.kwargs = kwargs or {} + super(WordEmbeddingSimilarityIndex, self).__init__() + + def most_similar(self, t1, topn=10): + if t1 not in self.keyedvectors.vocab: + logger.debug('an out-of-dictionary term "%s"', t1) + else: + most_similar = self.keyedvectors.most_similar(positive=[t1], topn=topn, **self.kwargs) + for t2, similarity in most_similar: + if similarity > self.threshold: + yield (t2, similarity**self.exponent) + + class SparseTermSimilarityMatrix(SaveLoad): """ Builds a sparse term similarity matrix using a term similarity index. diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index e117d5f283..eb63f4a213 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -14,10 +14,10 @@ from gensim import utils from gensim.models.word2vec import LineSentence -from gensim.models.fasttext import FastText as FT_gensim +from gensim.models.fasttext import FastText as FT_gensim, _unpack, _unpack_copy from gensim.models.wrappers.fasttext import FastTextKeyedVectors from gensim.models.wrappers.fasttext import FastText as FT_wrapper -from gensim.models.keyedvectors import Word2VecKeyedVectors +from gensim.models.keyedvectors import KeyedVectors from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences import gensim.models._fasttext_bin @@ -188,13 +188,11 @@ def test_norm_vectors_not_saved(self): model.save(tmpf) loaded_model = FT_gensim.load(tmpf) self.assertTrue(loaded_model.wv.vectors_norm is None) - self.assertTrue(loaded_model.wv.vectors_ngrams_norm is None) wv = model.wv wv.save(tmpf) loaded_kv = FastTextKeyedVectors.load(tmpf) self.assertTrue(loaded_kv.vectors_norm is None) - self.assertTrue(loaded_kv.vectors_ngrams_norm is None) def model_sanity(self, model): self.assertEqual(model.wv.vectors.shape, (len(model.wv.vocab), model.vector_size)) @@ -710,7 +708,10 @@ def online_sanity(self, model): others.append(x) self.assertTrue(all('terrorism' not in x for x in others)) model.build_vocab(others) + start_vecs = model.wv.vectors_vocab.copy() model.train(others, total_examples=model.corpus_count, epochs=model.epochs) + # checks that `vectors_vocab` has been changed by training + self.assertFalse(np.all(np.equal(start_vecs, model.wv.vectors_vocab))) # checks that `vectors` is different from `vectors_vocab` self.assertFalse(np.all(np.equal(model.wv.vectors, model.wv.vectors_vocab))) self.assertFalse('terrorism' in model.wv.vocab) @@ -757,7 +758,7 @@ def test_persistence_word2vec_format(self): tmpf = get_tmpfile('gensim_fasttext_w2v_format.tst') model = FT_gensim(sentences, min_count=1, size=12, bucket=BUCKET) model.wv.save_word2vec_format(tmpf, binary=True) - loaded_model_kv = Word2VecKeyedVectors.load_word2vec_format(tmpf, binary=True) + loaded_model_kv = KeyedVectors.load_word2vec_format(tmpf, binary=True) self.assertEqual(len(model.wv.vocab), len(loaded_model_kv.vocab)) self.assertTrue(np.allclose(model.wv['human'], loaded_model_kv['human'])) @@ -1024,7 +1025,7 @@ def test_sanity(self): trained = train_gensim() native = load_native() - self.assertEqual(trained.bucket, native.bucket) + self.assertEqual(trained.wv.bucket, native.wv.bucket) # # Only if match_gensim=True in init_post_load # @@ -1113,7 +1114,7 @@ def test_save_load_native(self): def test_load_native_pretrained(self): model = gensim.models.fasttext.load_facebook_model(datapath('toy-model-pretrained.bin')) - actual = model['monarchist'] + actual = model.wv['monarchist'] expected = np.array([0.76222, 1.0669, 0.7055, -0.090969, -0.53508]) self.assertTrue(np.allclose(expected, actual, atol=10e-4)) @@ -1509,6 +1510,63 @@ def test_cbow(self): self._check_load_fasttext_format(sg=0) +class TestFastTextKeyedVectors(unittest.TestCase): + def test_ft_kv_backward_compat_w_360(self): + kv = KeyedVectors.load(datapath("ft_kv_3.6.0.model.gz")) + ft_kv = FastTextKeyedVectors.load(datapath("ft_kv_3.6.0.model.gz")) + + expected = ['trees', 'survey', 'system', 'graph', 'interface'] + actual = [word for (word, similarity) in kv.most_similar("human", topn=5)] + + self.assertEqual(actual, expected) + + actual = [word for (word, similarity) in ft_kv.most_similar("human", topn=5)] + + self.assertEqual(actual, expected) + + +class UnpackTest(unittest.TestCase): + def test_copy_sanity(self): + m = np.array(range(9)) + m.shape = (3, 3) + hash2index = {10: 0, 11: 1, 12: 2} + + n = _unpack_copy(m, 25, hash2index) + self.assertTrue(np.all(m[0] == n[10])) + self.assertTrue(np.all(m[1] == n[11])) + self.assertTrue(np.all(m[2] == n[12])) + + def test_sanity(self): + m = np.array(range(9)) + m.shape = (3, 3) + hash2index = {10: 0, 11: 1, 12: 2} + + n = _unpack(m, 25, hash2index) + self.assertTrue(np.all(np.array([0, 1, 2]) == n[10])) + self.assertTrue(np.all(np.array([3, 4, 5]) == n[11])) + self.assertTrue(np.all(np.array([6, 7, 8]) == n[12])) + + def test_tricky(self): + m = np.array(range(9)) + m.shape = (3, 3) + hash2index = {1: 0, 0: 1, 12: 2} + + n = _unpack(m, 25, hash2index) + self.assertTrue(np.all(np.array([3, 4, 5]) == n[0])) + self.assertTrue(np.all(np.array([0, 1, 2]) == n[1])) + self.assertTrue(np.all(np.array([6, 7, 8]) == n[12])) + + def test_identity(self): + m = np.array(range(9)) + m.shape = (3, 3) + hash2index = {0: 0, 1: 1, 2: 2} + + n = _unpack(m, 25, hash2index) + self.assertTrue(np.all(np.array([0, 1, 2]) == n[0])) + self.assertTrue(np.all(np.array([3, 4, 5]) == n[1])) + self.assertTrue(np.all(np.array([6, 7, 8]) == n[2])) + + if __name__ == '__main__': logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) unittest.main() diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py index 43cea8625c..a69a4f2fe6 100644 --- a/gensim/test/test_keyedvectors.py +++ b/gensim/test/test_keyedvectors.py @@ -15,76 +15,19 @@ from mock import patch import numpy as np -from gensim.corpora import Dictionary -from gensim.models.keyedvectors import ( - KeyedVectors, WordEmbeddingSimilarityIndex, FastTextKeyedVectors, REAL, -) +from gensim.models.keyedvectors import KeyedVectors, FastTextKeyedVectors, REAL from gensim.test.utils import datapath import gensim.models.keyedvectors logger = logging.getLogger(__name__) -class TestWordEmbeddingSimilarityIndex(unittest.TestCase): - def setUp(self): - self.vectors = KeyedVectors.load_word2vec_format( - datapath('euclidean_vectors.bin'), binary=True, datatype=np.float64) - - def test_most_similar(self): - """Test most_similar returns expected results.""" - - # check the handling of out-of-dictionary terms - index = WordEmbeddingSimilarityIndex(self.vectors) - self.assertLess(0, len(list(index.most_similar(u"holiday", topn=10)))) - self.assertEqual(0, len(list(index.most_similar(u"out-of-dictionary term", topn=10)))) - - # check that the topn works as expected - index = WordEmbeddingSimilarityIndex(self.vectors) - results = list(index.most_similar(u"holiday", topn=10)) - self.assertLess(0, len(results)) - self.assertGreaterEqual(10, len(results)) - results = list(index.most_similar(u"holiday", topn=20)) - self.assertLess(10, len(results)) - self.assertGreaterEqual(20, len(results)) - - # check that the term itself is not returned - index = WordEmbeddingSimilarityIndex(self.vectors) - terms = [term for term, similarity in index.most_similar(u"holiday", topn=len(self.vectors.vocab))] - self.assertFalse(u"holiday" in terms) - - # check that the threshold works as expected - index = WordEmbeddingSimilarityIndex(self.vectors, threshold=0.0) - results = list(index.most_similar(u"holiday", topn=10)) - self.assertLess(0, len(results)) - self.assertGreaterEqual(10, len(results)) - - index = WordEmbeddingSimilarityIndex(self.vectors, threshold=1.0) - results = list(index.most_similar(u"holiday", topn=10)) - self.assertEqual(0, len(results)) - - # check that the exponent works as expected - index = WordEmbeddingSimilarityIndex(self.vectors, exponent=1.0) - first_similarities = np.array([similarity for term, similarity in index.most_similar(u"holiday", topn=10)]) - index = WordEmbeddingSimilarityIndex(self.vectors, exponent=2.0) - second_similarities = np.array([similarity for term, similarity in index.most_similar(u"holiday", topn=10)]) - self.assertTrue(np.allclose(first_similarities ** 2.0, second_similarities)) - - class TestKeyedVectors(unittest.TestCase): def setUp(self): self.vectors = KeyedVectors.load_word2vec_format( datapath('euclidean_vectors.bin'), binary=True, datatype=np.float64) - - def test_similarity_matrix(self): - """Test similarity_matrix returns expected results.""" - - documents = [[u"government", u"denied", u"holiday"], [u"holiday", u"slowing", u"hollingworth"]] - dictionary = Dictionary(documents) - similarity_matrix = self.vectors.similarity_matrix(dictionary).todense() - - # checking the existence of ones on the main diagonal - self.assertTrue( - (np.diag(similarity_matrix) == np.ones(similarity_matrix.shape[0])).all()) + self.model_path = datapath("w2v_keyedvectors_load_test.modeldata") + self.vocab_path = datapath("w2v_keyedvectors_load_test.vocab") def test_most_similar(self): """Test most_similar returns expected results.""" @@ -210,10 +153,6 @@ def test_rank(self): self.assertEqual(self.vectors.rank('war', 'war'), 1) self.assertEqual(self.vectors.rank('war', 'terrorism'), 3) - def test_wv_property(self): - """Test that the deprecated `wv` property returns `self`. To be removed in v4.0.0.""" - self.assertTrue(self.vectors is self.vectors) - def test_add_single(self): """Test that adding entity in a manual way works correctly.""" entities = ['___some_entity{}_not_present_in_keyed_vectors___'.format(i) for i in range(5)] @@ -295,94 +234,14 @@ def test_set_item(self): for ent, vector in zip(entities, vectors): self.assertTrue(np.allclose(self.vectors[ent], vector)) - def test_ft_kv_backward_compat_w_360(self): - kv = KeyedVectors.load(datapath("ft_kv_3.6.0.model.gz")) - ft_kv = FastTextKeyedVectors.load(datapath("ft_kv_3.6.0.model.gz")) - - expected = ['trees', 'survey', 'system', 'graph', 'interface'] - actual = [word for (word, similarity) in kv.most_similar("human", topn=5)] - - self.assertEqual(actual, expected) - - actual = [word for (word, similarity) in ft_kv.most_similar("human", topn=5)] - - self.assertEqual(actual, expected) - - -class L2NormTest(unittest.TestCase): - def test(self): - m = np.array(range(1, 10), dtype=np.float32) - m.shape = (3, 3) - - norm = gensim.models.keyedvectors._l2_norm(m) - self.assertFalse(np.allclose(m, norm)) - - gensim.models.keyedvectors._l2_norm(m, replace=True) - self.assertTrue(np.allclose(m, norm)) - - -class UnpackTest(unittest.TestCase): - def test_copy_sanity(self): - m = np.array(range(9)) - m.shape = (3, 3) - hash2index = {10: 0, 11: 1, 12: 2} - - n = gensim.models.keyedvectors._unpack_copy(m, 25, hash2index) - self.assertTrue(np.all(m[0] == n[10])) - self.assertTrue(np.all(m[1] == n[11])) - self.assertTrue(np.all(m[2] == n[12])) - - def test_sanity(self): - m = np.array(range(9)) - m.shape = (3, 3) - hash2index = {10: 0, 11: 1, 12: 2} - - n = gensim.models.keyedvectors._unpack(m, 25, hash2index) - self.assertTrue(np.all(np.array([0, 1, 2]) == n[10])) - self.assertTrue(np.all(np.array([3, 4, 5]) == n[11])) - self.assertTrue(np.all(np.array([6, 7, 8]) == n[12])) - - def test_tricky(self): - m = np.array(range(9)) - m.shape = (3, 3) - hash2index = {1: 0, 0: 1, 12: 2} - - n = gensim.models.keyedvectors._unpack(m, 25, hash2index) - self.assertTrue(np.all(np.array([3, 4, 5]) == n[0])) - self.assertTrue(np.all(np.array([0, 1, 2]) == n[1])) - self.assertTrue(np.all(np.array([6, 7, 8]) == n[12])) - - def test_identity(self): - m = np.array(range(9)) - m.shape = (3, 3) - hash2index = {0: 0, 1: 1, 2: 2} - - n = gensim.models.keyedvectors._unpack(m, 25, hash2index) - self.assertTrue(np.all(np.array([0, 1, 2]) == n[0])) - self.assertTrue(np.all(np.array([3, 4, 5]) == n[1])) - self.assertTrue(np.all(np.array([6, 7, 8]) == n[2])) - - -class Gensim320Test(unittest.TestCase): - def test(self): - path = datapath('old_keyedvectors_320.dat') - vectors = gensim.models.keyedvectors.KeyedVectors.load(path) - self.assertTrue(vectors.word_vec('computer') is not None) - - -class Word2VecKeyedVectorsTest(unittest.TestCase): - def setUp(self): - self.model_path = datapath("w2v_keyedvectors_load_test.modeldata") - self.vocab_path = datapath("w2v_keyedvectors_load_test.vocab") - def test_load_model_and_vocab_file_strict(self): - """Test loading model and vocab files which have decoding errors: strict mode""" + """Test loading model and voacab files which have decoding errors: strict mode""" with self.assertRaises(UnicodeDecodeError): gensim.models.KeyedVectors.load_word2vec_format( self.model_path, fvocab=self.vocab_path, binary=False, unicode_errors="strict") def test_load_model_and_vocab_file_replace(self): - """Test loading model and vocab files which have decoding errors: replace mode""" + """Test loading model and voacab files which have decoding errors: replace mode""" model = gensim.models.KeyedVectors.load_word2vec_format( self.model_path, fvocab=self.vocab_path, binary=False, unicode_errors="replace") self.assertEqual(model.vocab[u'ありがとう�'].count, 123) @@ -395,7 +254,7 @@ def test_load_model_and_vocab_file_replace(self): model.get_vector(u'どういたしまして�'), np.array([.1, .2, .3], dtype=np.float32))) def test_load_model_and_vocab_file_ignore(self): - """Test loading model and vocab files which have decoding errors: ignore mode""" + """Test loading model and voacab files which have decoding errors: ignore mode""" model = gensim.models.KeyedVectors.load_word2vec_format( self.model_path, fvocab=self.vocab_path, binary=False, unicode_errors="ignore") print(model.vocab.keys()) @@ -409,41 +268,11 @@ def test_load_model_and_vocab_file_ignore(self): model.get_vector(u'どういたしまして'), np.array([.1, .2, .3], dtype=np.float32))) -try: - import keras # noqa: F401 - - KERAS_INSTALLED = True -except ImportError: - KERAS_INSTALLED = False - - -@unittest.skipUnless(KERAS_INSTALLED, 'keras needs to be installed for this test') -class WordEmbeddingsKeyedVectorsTest(unittest.TestCase): - def setUp(self): - self.vectors = KeyedVectors.load_word2vec_format( - datapath('euclidean_vectors.bin'), binary=True, datatype=np.float64) - - def test_get_keras_embedding_word_index_none(self): - embedding_layer = self.vectors.get_keras_embedding() - self.assertEqual(self.vectors.vectors.shape, embedding_layer._initial_weights[0].shape) - self.assertTrue(np.array_equal( - self.vectors['is'], embedding_layer._initial_weights[0][self.vectors.vocab['is'].index, :])) - - def test_get_keras_embedding_word_index_passed(self): - word_index = {'is': 1, 'to': 2} - embedding_layer = self.vectors.get_keras_embedding(word_index=word_index) - self.assertEqual(embedding_layer._initial_weights[0].shape, (3, self.vectors.vectors.shape[1])) - self.assertTrue(np.array_equal( - self.vectors['is'], embedding_layer._initial_weights[0][1, :])) - - @patch('numpy.random.normal') - def test_get_keras_embedding_word_index_passed_with_oov_word(self, normal_func): - normal_func.return_value = np.zeros((3, self.vectors.vectors.shape[1])) - word_index = {'is': 1, 'not_a_real_word': 2} - embedding_layer = self.vectors.get_keras_embedding(word_index=word_index) - self.assertEqual(embedding_layer._initial_weights[0].shape, (3, self.vectors.vectors.shape[1])) - self.assertTrue( - np.array_equal(embedding_layer._initial_weights[0][2, :], np.zeros(self.vectors.vectors.shape[1]))) +class Gensim320Test(unittest.TestCase): + def test(self): + path = datapath('old_keyedvectors_320.dat') + vectors = gensim.models.keyedvectors.KeyedVectors.load(path) + self.assertTrue(vectors.word_vec('computer') is not None) if __name__ == '__main__': diff --git a/gensim/test/test_similarities.py b/gensim/test/test_similarities.py index 5fa441a2d5..77089a6f02 100644 --- a/gensim/test/test_similarities.py +++ b/gensim/test/test_similarities.py @@ -28,6 +28,7 @@ from gensim.test.utils import (datapath, get_tmpfile, common_texts as texts, common_dictionary as dictionary, common_corpus as corpus) from gensim.similarities import UniformTermSimilarityIndex +from gensim.similarities import WordEmbeddingSimilarityIndex from gensim.similarities import SparseTermSimilarityMatrix from gensim.similarities import LevenshteinSimilarityIndex from gensim.similarities.docsim import _nlargest @@ -1237,6 +1238,51 @@ def test_most_similar(self): self.assertTrue(scipy.sparse.issparse(similarity_matrix.matrix)) +class TestWordEmbeddingSimilarityIndex(unittest.TestCase): + def setUp(self): + self.vectors = KeyedVectors.load_word2vec_format( + datapath('euclidean_vectors.bin'), binary=True, datatype=numpy.float64) + + def test_most_similar(self): + """Test most_similar returns expected results.""" + + # check the handling of out-of-dictionary terms + index = WordEmbeddingSimilarityIndex(self.vectors) + self.assertLess(0, len(list(index.most_similar(u"holiday", topn=10)))) + self.assertEqual(0, len(list(index.most_similar(u"out-of-dictionary term", topn=10)))) + + # check that the topn works as expected + index = WordEmbeddingSimilarityIndex(self.vectors) + results = list(index.most_similar(u"holiday", topn=10)) + self.assertLess(0, len(results)) + self.assertGreaterEqual(10, len(results)) + results = list(index.most_similar(u"holiday", topn=20)) + self.assertLess(10, len(results)) + self.assertGreaterEqual(20, len(results)) + + # check that the term itself is not returned + index = WordEmbeddingSimilarityIndex(self.vectors) + terms = [term for term, similarity in index.most_similar(u"holiday", topn=len(self.vectors.vocab))] + self.assertFalse(u"holiday" in terms) + + # check that the threshold works as expected + index = WordEmbeddingSimilarityIndex(self.vectors, threshold=0.0) + results = list(index.most_similar(u"holiday", topn=10)) + self.assertLess(0, len(results)) + self.assertGreaterEqual(10, len(results)) + + index = WordEmbeddingSimilarityIndex(self.vectors, threshold=1.0) + results = list(index.most_similar(u"holiday", topn=10)) + self.assertEqual(0, len(results)) + + # check that the exponent works as expected + index = WordEmbeddingSimilarityIndex(self.vectors, exponent=1.0) + first_similarities = numpy.array([similarity for term, similarity in index.most_similar(u"holiday", topn=10)]) + index = WordEmbeddingSimilarityIndex(self.vectors, exponent=2.0) + second_similarities = numpy.array([similarity for term, similarity in index.most_similar(u"holiday", topn=10)]) + self.assertTrue(numpy.allclose(first_similarities**2.0, second_similarities)) + + if __name__ == '__main__': logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) unittest.main() diff --git a/setup.py b/setup.py index 09607d951a..02348c7d68 100644 --- a/setup.py +++ b/setup.py @@ -25,7 +25,6 @@ 'gensim.models.word2vec_inner': 'gensim/models/word2vec_inner.c', 'gensim.corpora._mmreader': 'gensim/corpora/_mmreader.c', 'gensim.models.fasttext_inner': 'gensim/models/fasttext_inner.c', - 'gensim.models._utils_any2vec': 'gensim/models/_utils_any2vec.c', 'gensim._matutils': 'gensim/_matutils.c', 'gensim.models.nmf_pgd': 'gensim/models/nmf_pgd.c', } From 38343d6336fb286f2da1ef2a5eb5cbf4fa766829 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 5 Dec 2019 19:18:13 -0800 Subject: [PATCH 03/60] mv FT, KV tests to right place --- gensim/test/test_fasttext.py | 251 ++++++++++++++++++++++++++++- gensim/test/test_keyedvectors.py | 95 +++++++++++ gensim/test/test_utils.py | 253 ------------------------------ gensim/test/test_utils_any2vec.py | 122 -------------- 4 files changed, 345 insertions(+), 376 deletions(-) delete mode 100644 gensim/test/test_utils_any2vec.py diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index eb63f4a213..a2f7aa3866 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -9,6 +9,8 @@ import os import subprocess import struct +import sys +import six import numpy as np @@ -20,7 +22,7 @@ from gensim.models.keyedvectors import KeyedVectors from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences import gensim.models._fasttext_bin - +from gensim.models.fasttext_inner import compute_ngrams, compute_ngrams_bytes, ft_hash_broken, ft_hash_bytes import gensim.models.fasttext @@ -1213,6 +1215,253 @@ def test_out_of_vocab(self): self.assertTrue(np.allclose(expected[longword], actual[longword], atol=1e-5)) +def hash_main(alg): + """Generate hash values for test from standard input.""" + + assert six.PY3, 'this only works under Py3' + + hashmap = { + 'cy_broken': ft_hash_broken, + 'cy_bytes': ft_hash_bytes, + } + try: + fun = hashmap[alg] + except KeyError: + raise KeyError('invalid alg: %r expected one of %r' % (alg, sorted(hashmap))) + + for line in sys.stdin: + if 'bytes' in alg: + words = line.encode('utf-8').rstrip().split(b' ') + else: + words = line.rstrip().split(' ') + for word in words: + print('u%r: %r,' % (word, fun(word))) + + +class HashTest(unittest.TestCase): + def setUp(self): + # + # I obtained these expected values using: + # + # $ echo word1 ... wordN | python -c 'from gensim.test.test_fasttext import hash_main;hash_main("alg")' # noqa: E501 + # + # where alg is one of py_bytes, py_broken, cy_bytes, cy_broken. + + # + self.expected = { + u'команда': 1725507386, + u'маленьких': 3011324125, + u'друзей': 737001801, + u'возит': 4225261911, + u'грузы': 1301826944, + u'всех': 706328732, + u'быстрей': 1379730754, + u'mysterious': 1903186891, + u'asteroid': 1988297200, + u'odyssey': 310195777, + u'introduction': 2848265721, + u'北海道': 4096045468, + u'札幌': 3909947444, + u'西区': 3653372632, + } + self.expected_broken = { + u'команда': 962806708, + u'маленьких': 3633597485, + u'друзей': 214728041, + u'возит': 3590926132, + u'грузы': 3674544745, + u'всех': 3931012458, + u'быстрей': 822471432, + u'mysterious': 1903186891, + u'asteroid': 1988297200, + u'odyssey': 310195777, + u'introduction': 2848265721, + u'北海道': 4017049120, + u'札幌': 1706980764, + u'西区': 1113327900, + } + + def test_cython(self): + actual = {k: ft_hash_bytes(k.encode('utf-8')) for k in self.expected} + self.assertEqual(self.expected, actual) + + def test_cython_broken(self): + actual = {k: ft_hash_broken(k) for k in self.expected} + self.assertEqual(self.expected_broken, actual) + + +# +# Run with: +# +# python -c 'import gensim.test.test_fasttext as t;t.ngram_main()' py_text 3 5 +# +def ngram_main(): + """Generate ngrams for tests from standard input.""" + + alg = sys.argv[1] + minn = int(sys.argv[2]) + maxn = int(sys.argv[3]) + + assert six.PY3, 'this only works under Py3' + assert minn <= maxn, 'expected sane command-line parameters' + + hashmap = { + 'cy_text': compute_ngrams, + 'cy_bytes': compute_ngrams_bytes, + } + try: + fun = hashmap[alg] + except KeyError: + raise KeyError('invalid alg: %r expected one of %r' % (alg, sorted(hashmap))) + + for line in sys.stdin: + word = line.rstrip('\n') + ngrams = fun(word, minn, maxn) + print("%r: %r," % (word, ngrams)) + + +class NgramsTest(unittest.TestCase): + def setUp(self): + self.expected_text = { + 'test': ['', '', ''], + 'at the': [ + '', + '', '' + ], + 'at\nthe': [ + '', + '', '' + ], + 'тест': ['<те', 'тес', 'ест', 'ст>', '<тес', 'тест', 'ест>', '<тест', 'тест>'], + 'テスト': ['<テス', 'テスト', 'スト>', '<テスト', 'テスト>', '<テスト>'], + '試し': ['<試し', '試し>', '<試し>'], + } + self.expected_bytes = { + 'test': [b'', b'est', b'est>', b'st>'], + 'at the': [ + b'', b'the', b'the>', b'he>' + ], + 'тест': [ + b'<\xd1\x82\xd0\xb5', b'<\xd1\x82\xd0\xb5\xd1\x81', b'<\xd1\x82\xd0\xb5\xd1\x81\xd1\x82', + b'\xd1\x82\xd0\xb5\xd1\x81', b'\xd1\x82\xd0\xb5\xd1\x81\xd1\x82', b'\xd1\x82\xd0\xb5\xd1\x81\xd1\x82>', + b'\xd0\xb5\xd1\x81\xd1\x82', b'\xd0\xb5\xd1\x81\xd1\x82>', b'\xd1\x81\xd1\x82>' + ], + 'テスト': [ + b'<\xe3\x83\x86\xe3\x82\xb9', b'<\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88', + b'<\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88>', b'\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88', + b'\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88>', b'\xe3\x82\xb9\xe3\x83\x88>' + ], + '試し': [b'<\xe8\xa9\xa6\xe3\x81\x97', b'<\xe8\xa9\xa6\xe3\x81\x97>', b'\xe8\xa9\xa6\xe3\x81\x97>'], + } + + self.expected_text_wide_unicode = { + '🚑🚒🚓🚕': [ + '<🚑🚒', '🚑🚒🚓', '🚒🚓🚕', '🚓🚕>', + '<🚑🚒🚓', '🚑🚒🚓🚕', '🚒🚓🚕>', '<🚑🚒🚓🚕', '🚑🚒🚓🚕>' + ], + } + self.expected_bytes_wide_unicode = { + '🚑🚒🚓🚕': [ + b'<\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92', + b'<\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93', + b'<\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95', + b'\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93', + b'\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95', + b'\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95>', + b'\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95', + b'\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95>', + b'\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95>' + ], + } + + def test_text_cy(self): + for word in self.expected_text: + expected = self.expected_text[word] + actual = compute_ngrams(word, 3, 5) + self.assertEqual(expected, actual) + + @unittest.skipIf(sys.maxunicode == 0xffff, "Python interpreter doesn't support UCS-4 (wide unicode)") + def test_text_cy_wide_unicode(self): + for word in self.expected_text_wide_unicode: + expected = self.expected_text_wide_unicode[word] + actual = compute_ngrams(word, 3, 5) + self.assertEqual(expected, actual) + + def test_bytes_cy(self): + for word in self.expected_bytes: + expected = self.expected_bytes[word] + actual = compute_ngrams_bytes(word, 3, 5) + self.assertEqual(expected, actual) + + expected_text = self.expected_text[word] + actual_text = [n.decode('utf-8') for n in actual] + self.assertEqual(sorted(expected_text), sorted(actual_text)) + + for word in self.expected_bytes_wide_unicode: + expected = self.expected_bytes_wide_unicode[word] + actual = compute_ngrams_bytes(word, 3, 5) + self.assertEqual(expected, actual) + + expected_text = self.expected_text_wide_unicode[word] + actual_text = [n.decode('utf-8') for n in actual] + self.assertEqual(sorted(expected_text), sorted(actual_text)) + + def test_fb(self): + """Test against results from Facebook's implementation.""" + with utils.open(datapath('fb-ngrams.txt'), 'r', encoding='utf-8') as fin: + fb = dict(_read_fb(fin)) + + for word, expected in fb.items(): + # + # The model was trained with minn=3, maxn=6 + # + actual = compute_ngrams(word, 3, 6) + self.assertEqual(sorted(expected), sorted(actual)) + + +def _read_fb(fin): + """Read ngrams from output of the FB utility.""" + # + # $ cat words.txt + # test + # at the + # at\nthe + # тест + # テスト + # 試し + # 🚑🚒🚓🚕 + # $ while read w; + # do + # echo ""; + # echo $w; + # ./fasttext print-ngrams gensim/test/test_data/crime-and-punishment.bin "$w"; + # echo ""; + # done < words.txt > gensim/test/test_data/fb-ngrams.txt + # + while fin: + line = fin.readline().rstrip() + if not line: + break + + assert line == '' + word = fin.readline().rstrip() + + fin.readline() # ignore this line, it contains an origin vector for the full term + + ngrams = [] + while True: + line = fin.readline().rstrip() + if line == '': + break + + columns = line.split(' ') + term = ' '.join(columns[:-5]) + ngrams.append(term) + + yield word, ngrams + + class ZeroBucketTest(unittest.TestCase): def test_in_vocab(self): model = train_gensim(bucket=0) diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py index a69a4f2fe6..3eb3ac28cc 100644 --- a/gensim/test/test_keyedvectors.py +++ b/gensim/test/test_keyedvectors.py @@ -275,6 +275,101 @@ def test(self): self.assertTrue(vectors.word_vec('computer') is not None) +def save_dict_to_word2vec_formated_file(fname, word2vec_dict): + + with gensim.utils.open(fname, "bw") as f: + + num_words = len(word2vec_dict) + vector_length = len(list(word2vec_dict.values())[0]) + + header = "%d %d\n" % (num_words, vector_length) + f.write(header.encode(encoding="ascii")) + + for word, vector in word2vec_dict.items(): + f.write(word.encode()) + f.write(' '.encode()) + f.write(np.array(vector).astype(np.float32).tobytes()) + + +class LoadWord2VecFormatTest(unittest.TestCase): + + def assert_dict_equal_to_model(self, d, m): + self.assertEqual(len(d), len(m.vocab)) + + for word in d.keys(): + self.assertSequenceEqual(list(d[word]), list(m[word])) + + def verify_load2vec_binary_result(self, w2v_dict, binary_chunk_size, limit): + tmpfile = gensim.test.utils.get_tmpfile("tmp_w2v") + save_dict_to_word2vec_formated_file(tmpfile, w2v_dict) + w2v_model = \ + gensim.models.keyedvectors._load_word2vec_format( + cls=gensim.models.KeyedVectors, + fname=tmpfile, + binary=True, + limit=limit, + binary_chunk_size=binary_chunk_size) + if limit is None: + limit = len(w2v_dict) + + w2v_keys_postprocessed = list(w2v_dict.keys())[:limit] + w2v_dict_postprocessed = {k.lstrip(): w2v_dict[k] for k in w2v_keys_postprocessed} + + self.assert_dict_equal_to_model(w2v_dict_postprocessed, w2v_model) + + def test_load_word2vec_format_basic(self): + w2v_dict = {"abc": [1, 2, 3], + "cde": [4, 5, 6], + "def": [7, 8, 9]} + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=None) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=None) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=None) + + w2v_dict = {"abc": [1, 2, 3], + "cdefg": [4, 5, 6], + "d": [7, 8, 9]} + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=None) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=None) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=None) + + def test_load_word2vec_format_limit(self): + w2v_dict = {"abc": [1, 2, 3], + "cde": [4, 5, 6], + "def": [7, 8, 9]} + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=1) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=1) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=1) + + w2v_dict = {"abc": [1, 2, 3], + "cde": [4, 5, 6], + "def": [7, 8, 9]} + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=2) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=2) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=2) + + w2v_dict = {"abc": [1, 2, 3], + "cdefg": [4, 5, 6], + "d": [7, 8, 9]} + + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=1) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=1) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=1) + + w2v_dict = {"abc": [1, 2, 3], + "cdefg": [4, 5, 6], + "d": [7, 8, 9]} + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=2) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=2) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=2) + + def test_load_word2vec_format_space_stripping(self): + w2v_dict = {"\nabc": [1, 2, 3], + "cdefdg": [4, 5, 6], + "\n\ndef": [7, 8, 9]} + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=None) + self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=1) + + if __name__ == '__main__': logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) unittest.main() diff --git a/gensim/test/test_utils.py b/gensim/test/test_utils.py index 910dea3fb1..424bdc62f7 100644 --- a/gensim/test/test_utils.py +++ b/gensim/test/test_utils.py @@ -18,8 +18,6 @@ from gensim import utils from gensim.test.utils import datapath, get_tmpfile -import gensim.models.utils_any2vec - class TestIsCorpus(unittest.TestCase): def test_None(self): @@ -268,257 +266,6 @@ def test_save_as_line_sentence_ru(self): self.assertEqual(sentences, ref_sentences) -def hash_main(alg): - """Generate hash values for test from standard input.""" - import sys - import six - - assert six.PY3, 'this only works under Py3' - - hashmap = { - 'cy_broken': gensim.models.utils_any2vec.ft_hash_broken, - 'cy_bytes': gensim.models.utils_any2vec.ft_hash_bytes, - } - try: - fun = hashmap[alg] - except KeyError: - raise KeyError('invalid alg: %r expected one of %r' % (alg, sorted(hashmap))) - - for line in sys.stdin: - if 'bytes' in alg: - words = line.encode('utf-8').rstrip().split(b' ') - else: - words = line.rstrip().split(' ') - for word in words: - print('u%r: %r,' % (word, fun(word))) - - -class HashTest(unittest.TestCase): - def setUp(self): - # - # I obtained these expected values using: - # - # $ echo word1 ... wordN | python -c 'from gensim.test.test_utils import hash_main;hash_main("alg")' # noqa: E501 - # - # where alg is one of py_bytes, py_broken, cy_bytes, cy_broken. - - # - self.expected = { - u'команда': 1725507386, - u'маленьких': 3011324125, - u'друзей': 737001801, - u'возит': 4225261911, - u'грузы': 1301826944, - u'всех': 706328732, - u'быстрей': 1379730754, - u'mysterious': 1903186891, - u'asteroid': 1988297200, - u'odyssey': 310195777, - u'introduction': 2848265721, - u'北海道': 4096045468, - u'札幌': 3909947444, - u'西区': 3653372632, - } - self.expected_broken = { - u'команда': 962806708, - u'маленьких': 3633597485, - u'друзей': 214728041, - u'возит': 3590926132, - u'грузы': 3674544745, - u'всех': 3931012458, - u'быстрей': 822471432, - u'mysterious': 1903186891, - u'asteroid': 1988297200, - u'odyssey': 310195777, - u'introduction': 2848265721, - u'北海道': 4017049120, - u'札幌': 1706980764, - u'西区': 1113327900, - } - - def test_cython(self): - actual = {k: gensim.models.utils_any2vec.ft_hash_bytes(k.encode('utf-8')) for k in self.expected} - self.assertEqual(self.expected, actual) - - def test_cython_broken(self): - actual = {k: gensim.models.utils_any2vec.ft_hash_broken(k) for k in self.expected} - self.assertEqual(self.expected_broken, actual) - - -# -# Run with: -# -# python -c 'import gensim.test.test_utils as t;t.ngram_main()' py_text 3 5 -# -def ngram_main(): - """Generate ngrams for tests from standard input.""" - import sys - import six - - alg = sys.argv[1] - minn = int(sys.argv[2]) - maxn = int(sys.argv[3]) - - assert six.PY3, 'this only works under Py3' - assert minn <= maxn, 'expected sane command-line parameters' - - hashmap = { - 'cy_text': gensim.models.utils_any2vec.compute_ngrams, - 'cy_bytes': gensim.models.utils_any2vec.compute_ngrams_bytes, - } - try: - fun = hashmap[alg] - except KeyError: - raise KeyError('invalid alg: %r expected one of %r' % (alg, sorted(hashmap))) - - for line in sys.stdin: - word = line.rstrip('\n') - ngrams = fun(word, minn, maxn) - print("%r: %r," % (word, ngrams)) - - -class NgramsTest(unittest.TestCase): - def setUp(self): - self.expected_text = { - 'test': ['', '', ''], - 'at the': [ - '', - '', '' - ], - 'at\nthe': [ - '', - '', '' - ], - 'тест': ['<те', 'тес', 'ест', 'ст>', '<тес', 'тест', 'ест>', '<тест', 'тест>'], - 'テスト': ['<テス', 'テスト', 'スト>', '<テスト', 'テスト>', '<テスト>'], - '試し': ['<試し', '試し>', '<試し>'], - } - self.expected_bytes = { - 'test': [b'', b'est', b'est>', b'st>'], - 'at the': [ - b'', b'the', b'the>', b'he>' - ], - 'тест': [ - b'<\xd1\x82\xd0\xb5', b'<\xd1\x82\xd0\xb5\xd1\x81', b'<\xd1\x82\xd0\xb5\xd1\x81\xd1\x82', - b'\xd1\x82\xd0\xb5\xd1\x81', b'\xd1\x82\xd0\xb5\xd1\x81\xd1\x82', b'\xd1\x82\xd0\xb5\xd1\x81\xd1\x82>', - b'\xd0\xb5\xd1\x81\xd1\x82', b'\xd0\xb5\xd1\x81\xd1\x82>', b'\xd1\x81\xd1\x82>' - ], - 'テスト': [ - b'<\xe3\x83\x86\xe3\x82\xb9', b'<\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88', - b'<\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88>', b'\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88', - b'\xe3\x83\x86\xe3\x82\xb9\xe3\x83\x88>', b'\xe3\x82\xb9\xe3\x83\x88>' - ], - '試し': [b'<\xe8\xa9\xa6\xe3\x81\x97', b'<\xe8\xa9\xa6\xe3\x81\x97>', b'\xe8\xa9\xa6\xe3\x81\x97>'], - } - - self.expected_text_wide_unicode = { - '🚑🚒🚓🚕': [ - '<🚑🚒', '🚑🚒🚓', '🚒🚓🚕', '🚓🚕>', - '<🚑🚒🚓', '🚑🚒🚓🚕', '🚒🚓🚕>', '<🚑🚒🚓🚕', '🚑🚒🚓🚕>' - ], - } - self.expected_bytes_wide_unicode = { - '🚑🚒🚓🚕': [ - b'<\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92', - b'<\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93', - b'<\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95', - b'\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93', - b'\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95', - b'\xf0\x9f\x9a\x91\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95>', - b'\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95', - b'\xf0\x9f\x9a\x92\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95>', - b'\xf0\x9f\x9a\x93\xf0\x9f\x9a\x95>' - ], - } - - def test_text_cy(self): - for word in self.expected_text: - expected = self.expected_text[word] - actual = gensim.models.utils_any2vec.compute_ngrams(word, 3, 5) - self.assertEqual(expected, actual) - - @unittest.skipIf(sys.maxunicode == 0xffff, "Python interpreter doesn't support UCS-4 (wide unicode)") - def test_text_cy_wide_unicode(self): - for word in self.expected_text_wide_unicode: - expected = self.expected_text_wide_unicode[word] - actual = gensim.models.utils_any2vec.compute_ngrams(word, 3, 5) - self.assertEqual(expected, actual) - - def test_bytes_cy(self): - for word in self.expected_bytes: - expected = self.expected_bytes[word] - actual = gensim.models.utils_any2vec.compute_ngrams_bytes(word, 3, 5) - self.assertEqual(expected, actual) - - expected_text = self.expected_text[word] - actual_text = [n.decode('utf-8') for n in actual] - self.assertEqual(sorted(expected_text), sorted(actual_text)) - - for word in self.expected_bytes_wide_unicode: - expected = self.expected_bytes_wide_unicode[word] - actual = gensim.models.utils_any2vec.compute_ngrams_bytes(word, 3, 5) - self.assertEqual(expected, actual) - - expected_text = self.expected_text_wide_unicode[word] - actual_text = [n.decode('utf-8') for n in actual] - self.assertEqual(sorted(expected_text), sorted(actual_text)) - - def test_fb(self): - """Test against results from Facebook's implementation.""" - with utils.open(datapath('fb-ngrams.txt'), 'r', encoding='utf-8') as fin: - fb = dict(_read_fb(fin)) - - for word, expected in fb.items(): - # - # The model was trained with minn=3, maxn=6 - # - actual = gensim.models.utils_any2vec.compute_ngrams(word, 3, 6) - self.assertEqual(sorted(expected), sorted(actual)) - - -def _read_fb(fin): - """Read ngrams from output of the FB utility.""" - # - # $ cat words.txt - # test - # at the - # at\nthe - # тест - # テスト - # 試し - # 🚑🚒🚓🚕 - # $ while read w; - # do - # echo ""; - # echo $w; - # ./fasttext print-ngrams gensim/test/test_data/crime-and-punishment.bin "$w"; - # echo ""; - # done < words.txt > gensim/test/test_data/fb-ngrams.txt - # - while fin: - line = fin.readline().rstrip() - if not line: - break - - assert line == '' - word = fin.readline().rstrip() - - fin.readline() # ignore this line, it contains an origin vector for the full term - - ngrams = [] - while True: - line = fin.readline().rstrip() - if line == '': - break - - columns = line.split(' ') - term = ' '.join(columns[:-5]) - ngrams.append(term) - - yield word, ngrams - - if __name__ == '__main__': logging.root.setLevel(logging.WARNING) unittest.main() diff --git a/gensim/test/test_utils_any2vec.py b/gensim/test/test_utils_any2vec.py deleted file mode 100644 index f4c5c2c430..0000000000 --- a/gensim/test/test_utils_any2vec.py +++ /dev/null @@ -1,122 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Copyright (C) 2017 Radim Rehurek -# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html - -""" -Automated tests for checking utils_any2vec functionality. -""" - -import logging -import unittest - -import numpy as np - -import gensim.utils -import gensim.test.utils - -import gensim.models.utils_any2vec - - -logger = logging.getLogger(__name__) - - -def save_dict_to_word2vec_formated_file(fname, word2vec_dict): - - with gensim.utils.open(fname, "bw") as f: - - num_words = len(word2vec_dict) - vector_length = len(list(word2vec_dict.values())[0]) - - header = "%d %d\n" % (num_words, vector_length) - f.write(header.encode(encoding="ascii")) - - for word, vector in word2vec_dict.items(): - f.write(word.encode()) - f.write(' '.encode()) - f.write(np.array(vector).astype(np.float32).tobytes()) - - -class LoadWord2VecFormatTest(unittest.TestCase): - - def assert_dict_equal_to_model(self, d, m): - self.assertEqual(len(d), len(m.vocab)) - - for word in d.keys(): - self.assertSequenceEqual(list(d[word]), list(m[word])) - - def verify_load2vec_binary_result(self, w2v_dict, binary_chunk_size, limit): - tmpfile = gensim.test.utils.get_tmpfile("tmp_w2v") - save_dict_to_word2vec_formated_file(tmpfile, w2v_dict) - w2v_model = \ - gensim.models.utils_any2vec._load_word2vec_format( - cls=gensim.models.KeyedVectors, - fname=tmpfile, - binary=True, - limit=limit, - binary_chunk_size=binary_chunk_size) - if limit is None: - limit = len(w2v_dict) - - w2v_keys_postprocessed = list(w2v_dict.keys())[:limit] - w2v_dict_postprocessed = {k.lstrip(): w2v_dict[k] for k in w2v_keys_postprocessed} - - self.assert_dict_equal_to_model(w2v_dict_postprocessed, w2v_model) - - def test_load_word2vec_format_basic(self): - w2v_dict = {"abc": [1, 2, 3], - "cde": [4, 5, 6], - "def": [7, 8, 9]} - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=None) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=None) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=None) - - w2v_dict = {"abc": [1, 2, 3], - "cdefg": [4, 5, 6], - "d": [7, 8, 9]} - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=None) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=None) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=None) - - def test_load_word2vec_format_limit(self): - w2v_dict = {"abc": [1, 2, 3], - "cde": [4, 5, 6], - "def": [7, 8, 9]} - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=1) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=1) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=1) - - w2v_dict = {"abc": [1, 2, 3], - "cde": [4, 5, 6], - "def": [7, 8, 9]} - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=2) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=2) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=2) - - w2v_dict = {"abc": [1, 2, 3], - "cdefg": [4, 5, 6], - "d": [7, 8, 9]} - - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=1) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=1) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=1) - - w2v_dict = {"abc": [1, 2, 3], - "cdefg": [4, 5, 6], - "d": [7, 8, 9]} - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=2) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=16, limit=2) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=1024, limit=2) - - def test_load_word2vec_format_space_stripping(self): - w2v_dict = {"\nabc": [1, 2, 3], - "cdefdg": [4, 5, 6], - "\n\ndef": [7, 8, 9]} - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=None) - self.verify_load2vec_binary_result(w2v_dict, binary_chunk_size=5, limit=1) - - -if __name__ == '__main__': - logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) - unittest.main() From a255e8c21a7fb4111f288caa8eb7a094fd28bf6f Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 5 Dec 2019 11:58:02 -0800 Subject: [PATCH 04/60] rm deprecations, obsolete refs/tests, delete_temporary_training_data, update usages --- docs/src/apiref.rst | 2 - docs/src/models/_utils_any2vec.rst | 9 -- docs/src/models/utils_any2vec.rst | 9 -- gensim/models/base_any2vec.py | 197 ----------------------------- gensim/models/doc2vec.py | 53 +------- gensim/models/keyedvectors.py | 2 +- gensim/models/poincare.py | 180 +++++--------------------- gensim/models/word2vec.py | 89 ------------- gensim/similarities/docsim.py | 4 +- gensim/similarities/index.py | 3 +- gensim/similarities/nmslib.py | 4 +- gensim/test/test_doc2vec.py | 34 ----- gensim/test/test_poincare.py | 36 +++--- gensim/test/test_word2vec.py | 50 ++------ 14 files changed, 72 insertions(+), 600 deletions(-) delete mode 100644 docs/src/models/_utils_any2vec.rst delete mode 100644 docs/src/models/utils_any2vec.rst diff --git a/docs/src/apiref.rst b/docs/src/apiref.rst index 6218336b06..e20c1e2f1f 100644 --- a/docs/src/apiref.rst +++ b/docs/src/apiref.rst @@ -53,8 +53,6 @@ Modules: models/coherencemodel models/basemodel models/callbacks - models/utils_any2vec - models/_utils_any2vec models/word2vec_inner models/doc2vec_inner models/fasttext_inner diff --git a/docs/src/models/_utils_any2vec.rst b/docs/src/models/_utils_any2vec.rst deleted file mode 100644 index 46e5541ec3..0000000000 --- a/docs/src/models/_utils_any2vec.rst +++ /dev/null @@ -1,9 +0,0 @@ -:mod:`models._utils_any2vec` -- Cython utils for any2vec models -=============================================================== - -.. automodule:: gensim.models._utils_any2vec - :synopsis: Cython utils for any2vec models - :members: - :inherited-members: - :undoc-members: - :show-inheritance: diff --git a/docs/src/models/utils_any2vec.rst b/docs/src/models/utils_any2vec.rst deleted file mode 100644 index 123ee265e6..0000000000 --- a/docs/src/models/utils_any2vec.rst +++ /dev/null @@ -1,9 +0,0 @@ -:mod:`models.utils_any2vec` -- Utils for any2vec models -======================================================= - -.. automodule:: gensim.models.utils_any2vec - :synopsis: Utils for any2vec models - :members: - :inherited-members: - :undoc-members: - :show-inheritance: diff --git a/gensim/models/base_any2vec.py b/gensim/models/base_any2vec.py index 4a58257cf0..f6d4f77090 100644 --- a/gensim/models/base_any2vec.py +++ b/gensim/models/base_any2vec.py @@ -41,7 +41,6 @@ from gensim import matutils from numpy import float32 as REAL, ones, random, dtype from types import GeneratorType -from gensim.utils import deprecated import os import copy @@ -754,117 +753,6 @@ def __init__(self, sentences=None, corpus_file=None, workers=3, vector_size=100, "and is not stored as part of the model. Model initialized without sentences. " "trim_rule provided, if any, will be ignored.") - # for backward compatibility (aliases pointing to corresponding variables in trainables, vocabulary) - @property - @deprecated("Attribute will be removed in 4.0.0, use self.epochs instead") - def iter(self): - return self.epochs - - @iter.setter - @deprecated("Attribute will be removed in 4.0.0, use self.epochs instead") - def iter(self, value): - self.epochs = value - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.syn1 instead") - def syn1(self): - return self.trainables.syn1 - - @syn1.setter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.syn1 instead") - def syn1(self, value): - self.trainables.syn1 = value - - @syn1.deleter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.syn1 instead") - def syn1(self): - del self.trainables.syn1 - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.syn1neg instead") - def syn1neg(self): - return self.trainables.syn1neg - - @syn1neg.setter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.syn1neg instead") - def syn1neg(self, value): - self.trainables.syn1neg = value - - @syn1neg.deleter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.syn1neg instead") - def syn1neg(self): - del self.trainables.syn1neg - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_lockf instead") - def syn0_lockf(self): - return self.trainables.vectors_lockf - - @syn0_lockf.setter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_lockf instead") - def syn0_lockf(self, value): - self.trainables.vectors_lockf = value - - @syn0_lockf.deleter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.vectors_lockf instead") - def syn0_lockf(self): - del self.trainables.vectors_lockf - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.layer1_size instead") - def layer1_size(self): - return self.trainables.layer1_size - - @layer1_size.setter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.layer1_size instead") - def layer1_size(self, value): - self.trainables.layer1_size = value - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.hashfxn instead") - def hashfxn(self): - return self.trainables.hashfxn - - @hashfxn.setter - @deprecated("Attribute will be removed in 4.0.0, use self.trainables.hashfxn instead") - def hashfxn(self, value): - self.trainables.hashfxn = value - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.vocabulary.sample instead") - def sample(self): - return self.vocabulary.sample - - @sample.setter - @deprecated("Attribute will be removed in 4.0.0, use self.vocabulary.sample instead") - def sample(self, value): - self.vocabulary.sample = value - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.vocabulary.min_count instead") - def min_count(self): - return self.vocabulary.min_count - - @min_count.setter - @deprecated("Attribute will be removed in 4.0.0, use self.vocabulary.min_count instead") - def min_count(self, value): - self.vocabulary.min_count = value - - @property - @deprecated("Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead") - def cum_table(self): - return self.vocabulary.cum_table - - @cum_table.setter - @deprecated("Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead") - def cum_table(self, value): - self.vocabulary.cum_table = value - - @cum_table.deleter - @deprecated("Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead") - def cum_table(self): - del self.vocabulary.cum_table - def __str__(self): """Get a human readable representation of the object. @@ -1367,88 +1255,3 @@ def _log_train_end(self, raw_word_count, trained_word_count, total_elapsed, job_ "training on a %i raw words (%i effective words) took %.1fs, %.0f effective words/s", raw_word_count, trained_word_count, total_elapsed, trained_word_count / total_elapsed ) - - # for backward compatibility - @deprecated("Method will be removed in 4.0.0, use self.wv.most_similar() instead") - def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=None, indexer=None): - """Deprecated, use self.wv.most_similar() instead. - - Refer to the documentation for :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.most_similar`. - - """ - return self.wv.most_similar(positive, negative, topn, restrict_vocab, indexer) - - @deprecated("Method will be removed in 4.0.0, use self.wv.wmdistance() instead") - def wmdistance(self, document1, document2): - """Deprecated, use self.wv.wmdistance() instead. - - Refer to the documentation for :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.wmdistance`. - - """ - return self.wv.wmdistance(document1, document2) - - @deprecated("Method will be removed in 4.0.0, use self.wv.most_similar_cosmul() instead") - def most_similar_cosmul(self, positive=None, negative=None, topn=10): - """Deprecated, use self.wv.most_similar_cosmul() instead. - - Refer to the documentation for - :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.most_similar_cosmul`. - - """ - return self.wv.most_similar_cosmul(positive, negative, topn) - - @deprecated("Method will be removed in 4.0.0, use self.wv.similar_by_word() instead") - def similar_by_word(self, word, topn=10, restrict_vocab=None): - """Deprecated, use self.wv.similar_by_word() instead. - - Refer to the documentation for :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similar_by_word`. - - """ - return self.wv.similar_by_word(word, topn, restrict_vocab) - - @deprecated("Method will be removed in 4.0.0, use self.wv.similar_by_vector() instead") - def similar_by_vector(self, vector, topn=10, restrict_vocab=None): - """Deprecated, use self.wv.similar_by_vector() instead. - - Refer to the documentation for :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similar_by_vector`. - - """ - return self.wv.similar_by_vector(vector, topn, restrict_vocab) - - @deprecated("Method will be removed in 4.0.0, use self.wv.doesnt_match() instead") - def doesnt_match(self, words): - """Deprecated, use self.wv.doesnt_match() instead. - - Refer to the documentation for :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.doesnt_match`. - - """ - return self.wv.doesnt_match(words) - - @deprecated("Method will be removed in 4.0.0, use self.wv.similarity() instead") - def similarity(self, w1, w2): - """Deprecated, use self.wv.similarity() instead. - - Refer to the documentation for :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similarity`. - - """ - return self.wv.similarity(w1, w2) - - @deprecated("Method will be removed in 4.0.0, use self.wv.n_similarity() instead") - def n_similarity(self, ws1, ws2): - """Deprecated, use self.wv.n_similarity() instead. - - Refer to the documentation for :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.n_similarity`. - - """ - return self.wv.n_similarity(ws1, ws2) - - @deprecated("Method will be removed in 4.0.0, use self.wv.evaluate_word_pairs() instead") - def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, - case_insensitive=True, dummy4unknown=False): - """Deprecated, use self.wv.evaluate_word_pairs() instead. - - Refer to the documentation for - :meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.evaluate_word_pairs`. - - """ - return self.wv.evaluate_word_pairs(pairs, delimiter, restrict_vocab, case_insensitive, dummy4unknown) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index f7c1e55cdd..5cf392b67f 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -146,13 +146,6 @@ 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): - """Deprecated, use :class:`~gensim.models.doc2vec.TaggedDocument` instead.""" - pass - - 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. @@ -302,20 +295,6 @@ def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_wo List of callbacks that need to be executed/run at specific stages during training. """ - if 'sentences' in kwargs: - raise DeprecationWarning( - "Parameter 'sentences' was renamed to 'documents', and will be removed in 4.0.0, " - "use 'documents' instead." - ) - - if 'iter' in kwargs: - warnings.warn("The parameter `iter` is deprecated, will be removed in 4.0.0, use `epochs` instead.") - kwargs['epochs'] = kwargs['iter'] - - if 'size' in kwargs: - warnings.warn("The parameter `size` is deprecated, will be removed in 4.0.0, use `vector_size` instead.") - kwargs['vector_size'] = kwargs['size'] - super(Doc2Vec, self).__init__( sg=(1 + dm) % 2, null_word=dm_concat, @@ -645,9 +624,6 @@ def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps Number of times to train the new document. Larger values take more time, but may improve quality and run-to-run stability of inferred vectors. If unspecified, the `epochs` value from model initialization will be reused. - steps : int, optional, deprecated - Previous name for `epochs`, still available for now for backward compatibility: if - `epochs` is unspecified but `steps` is, the `steps` value will be used. Returns ------- @@ -660,7 +636,7 @@ def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps alpha = alpha or self.alpha min_alpha = min_alpha or self.min_alpha - epochs = epochs or steps or self.epochs + epochs = epochs or self.epochs doctag_vectors, doctag_locks = self.trainables.get_doctag_trainables(doc_words, self.docvecs.vector_size) doctag_indexes = [0] @@ -751,33 +727,6 @@ def __str__(self): 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. - - Parameters - ---------- - keep_doctags_vectors : bool, optional - Set to False if you don't want to save doctags vectors. In this case you will not be able to use - :meth:`~gensim.models.keyedvectors.Doc2VecKeyedVectors.most_similar`, - :meth:`~gensim.models.keyedvectors.Doc2VecKeyedVectors.similarity`, etc methods. - keep_inference : bool, optional - Set to False if you don't want to store parameters that are used for - :meth:`~gensim.models.doc2vec.Doc2Vec.infer_vector` method. - - """ - if not keep_inference: - if hasattr(self.trainables, 'syn1'): - del self.trainables.syn1 - if hasattr(self.trainables, 'syn1neg'): - del self.trainables.syn1neg - if hasattr(self.trainables, 'vectors_lockf'): - del self.trainables.vectors_lockf - self.model_trimmed_post_training = True - if self.docvecs and hasattr(self.docvecs, 'vectors_docs') and not keep_doctags_vectors: - del self.docvecs.vectors_docs - if self.docvecs and hasattr(self.trainables, 'vectors_docs_lockf'): - del self.trainables.vectors_docs_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 in the same format used by the original C word2vec-tool. diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 4229a58105..ef93e01ef0 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -1523,7 +1523,7 @@ def distances(self, d1, other_docs=()): other_vectors = self.vectors_docs else: other_vectors = self[other_docs] - return 1 - WordEmbeddingsKeyedVectors.cosine_similarities(input_vector, other_vectors) + return 1 - KeyedVectors.cosine_similarities(input_vector, other_vectors) def similarity_unseen_docs(self, model, doc_words1, doc_words2, alpha=None, min_alpha=None, steps=None): """Compute cosine similarity between two post-bulk out of training documents. diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index f161073f1a..906c23c27c 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -237,13 +237,13 @@ def build_vocab(self, relations, update=False): def _init_embeddings(self): """Randomly initialize vectors for the items in the vocab.""" shape = (len(self.kv.index2word), self.size) - self.kv.syn0 = self._np_random.uniform(self.init_range[0], self.init_range[1], shape).astype(self.dtype) + self.kv.vectors = self._np_random.uniform(self.init_range[0], self.init_range[1], shape).astype(self.dtype) def _update_embeddings(self, old_index2word_len): """Randomly initialize vectors for the items in the additional vocab.""" shape = (len(self.kv.index2word) - old_index2word_len, self.size) v = self._np_random.uniform(self.init_range[0], self.init_range[1], shape).astype(self.dtype) - self.kv.syn0 = np.concatenate([self.kv.syn0, v]) + self.kv.vectors = np.concatenate([self.kv.vectors, v]) def _init_node_probabilities(self): """Initialize a-priori probabilities.""" @@ -460,8 +460,8 @@ def _prepare_training_batch(self, relations, all_negatives, check_gradients=Fals indices_v.append(v) indices_v.extend(negatives) - vectors_u = self.kv.syn0[indices_u] - vectors_v = self.kv.syn0[indices_v].reshape((batch_size, 1 + self.negative, self.size)) + vectors_u = self.kv.vectors[indices_u] + vectors_v = self.kv.vectors[indices_v].reshape((batch_size, 1 + self.negative, self.size)) vectors_v = vectors_v.swapaxes(0, 1).swapaxes(1, 2) batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v, self.regularization_coeff) batch.compute_all() @@ -498,7 +498,7 @@ def _check_gradients(self, relations, all_negatives, batch, tol=1e-8): for i, (relation, negatives) in enumerate(zip(relations, all_negatives)): u, v = relation auto_gradients = self._loss_grad( - np.vstack((self.kv.syn0[u], self.kv.syn0[[v] + negatives])), self.regularization_coeff) + np.vstack((self.kv.vectors[u], self.kv.vectors[[v] + negatives])), self.regularization_coeff) computed_gradients = np.vstack((batch.gradients_u[:, i], batch.gradients_v[:, :, i])) diff = np.abs(auto_gradients - computed_gradients).max() if diff > max_diff: @@ -593,16 +593,16 @@ def _update_vectors_batch(self, batch): u_updates = (self.alpha * (batch.alpha ** 2) / 4 * grad_u).T self._handle_duplicates(u_updates, indices_u) - self.kv.syn0[indices_u] -= u_updates - self.kv.syn0[indices_u] = self._clip_vectors(self.kv.syn0[indices_u], self.epsilon) + self.kv.vectors[indices_u] -= u_updates + self.kv.vectors[indices_u] = self._clip_vectors(self.kv.vectors[indices_u], self.epsilon) v_updates = self.alpha * (batch.beta ** 2)[:, np.newaxis] / 4 * grad_v v_updates = v_updates.swapaxes(1, 2).swapaxes(0, 1) v_updates = v_updates.reshape(((1 + self.negative) * batch_size, self.size)) self._handle_duplicates(v_updates, indices_v) - self.kv.syn0[indices_v] -= v_updates - self.kv.syn0[indices_v] = self._clip_vectors(self.kv.syn0[indices_v], self.epsilon) + self.kv.vectors[indices_v] -= v_updates + self.kv.vectors[indices_v] = self._clip_vectors(self.kv.vectors[indices_v], self.epsilon) def train(self, epochs, batch_size=10, print_every=1000, check_gradients_every=None): """Train Poincare embeddings using loaded data and model parameters. @@ -864,143 +864,33 @@ class PoincareKeyedVectors(KeyedVectors): Used to perform operations on the vectors such as vector lookup, distance calculations etc. + (May be used to save/load final vectors in the plain word2vec format, via the inherited + methods save_word2vec_format() and load_word2vec_format().) + + Examples + -------- + .. sourcecode:: pycon + + >>> from gensim.test.utils import datapath + >>> + >>> # Read the sample relations file and train the model + >>> relations = PoincareRelations(file_path=datapath('poincare_hypernyms_large.tsv')) + >>> model = PoincareModel(train_data=relations) + >>> model.train(epochs=50) + >>> + >>> # Query the trained model. + >>> wv = model.kv.word_vec('kangaroo.n.01') + """ def __init__(self, vector_size): super(PoincareKeyedVectors, self).__init__(vector_size) self.max_distance = 0 - self.index2word = [] - self.vocab = {} - - @property - def vectors(self): - return self.syn0 - - @vectors.setter - def vectors(self, value): - self.syn0 = value - - @property - def index2entity(self): - return self.index2word - - @index2entity.setter - def index2entity(self, value): - self.index2word = value - - def word_vec(self, word): - """Get the word's representations in vector space, as a 1D numpy array. - Examples - -------- - .. sourcecode:: pycon - - >>> from gensim.test.utils import datapath - >>> - >>> # Read the sample relations file and train the model - >>> relations = PoincareRelations(file_path=datapath('poincare_hypernyms_large.tsv')) - >>> model = PoincareModel(train_data=relations) - >>> model.train(epochs=50) - >>> - >>> # Query the trained model. - >>> wv = model.kv.word_vec('kangaroo.n.01') - - """ - return super(PoincareKeyedVectors, self).get_vector(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`. - - Examples - -------- - .. sourcecode:: pycon - - >>> from gensim.test.utils import datapath - >>> - >>> # Read the sample relations file and train the model - >>> relations = PoincareRelations(file_path=datapath('poincare_hypernyms_large.tsv')) - >>> model = PoincareModel(train_data=relations) - >>> model.train(epochs=50) - >>> - >>> # Which term is closer to 'kangaroo' than 'metatherian' is to 'kangaroo'? - >>> model.kv.words_closer_than('kangaroo.n.01', 'metatherian.n.01') - [u'marsupial.n.01', u'phalanger.n.01'] - - """ - return super(PoincareKeyedVectors, self).closer_than(w1, w2) - - 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, using :func:`~gensim.models.utils_any2vec._save_word2vec_format`. - - Parameters - ---------- - fname : str - Path to file that will be used for storing. - fvocab : str, 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 - Explicitly specify total number of vectors - (in case word vectors are appended with document vectors afterwards). - - """ - _save_word2vec_format(fname, self.vocab, self.syn0, 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. - Use :func:`~gensim.models.utils_any2vec._load_word2vec_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. - - 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.poincare.PoincareModel` - Loaded Poincare model. - - """ - return _load_word2vec_format( - cls, fname, fvocab=fvocab, binary=binary, encoding=encoding, unicode_errors=unicode_errors, - limit=limit, datatype=datatype) + def _load_specials(self, *args, **kwargs): + super(PoincareKeyedVectors, self)._load_specials(*args, **kwargs) + # fixup rename of syn0 + if not hasattr(self, 'vectors'): + self.vectors = self.__dict__.pop('syn0') @staticmethod def vector_distance(vector_1, vector_2): @@ -1063,7 +953,7 @@ def closest_child(self, node): """ all_distances = self.distances(node) - all_norms = np.linalg.norm(self.syn0, axis=1) + all_norms = np.linalg.norm(self.vectors, axis=1) node_norm = all_norms[self.vocab[node].index] mask = node_norm >= all_norms if mask.all(): # No nodes lower in the hierarchy @@ -1088,7 +978,7 @@ def closest_parent(self, node): """ all_distances = self.distances(node) - all_norms = np.linalg.norm(self.syn0, axis=1) + all_norms = np.linalg.norm(self.vectors, axis=1) node_norm = all_norms[self.vocab[node].index] mask = node_norm <= all_norms if mask.all(): # No nodes higher in the hierarchy @@ -1332,10 +1222,10 @@ def distances(self, node_or_vector, other_nodes=()): else: input_vector = node_or_vector if not other_nodes: - other_vectors = self.syn0 + other_vectors = self.vectors else: other_indices = [self.vocab[node].index for node in other_nodes] - other_vectors = self.syn0[other_indices] + other_vectors = self.vectors[other_indices] return self.vector_distance_batch(input_vector, other_vectors) def norm(self, node_or_vector): diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index eea4fcd86c..758c4380f0 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -147,7 +147,6 @@ from scipy.special import expit from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc -from gensim.utils import deprecated from six import iteritems, itervalues, string_types from six.moves import range @@ -923,22 +922,6 @@ def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='ut self.trainables.vectors_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0=no changes logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.vectors.shape, fname) - @deprecated("Method will be removed in 4.0.0, use self.wv.__getitem__() instead") - def __getitem__(self, words): - """Deprecated. Use `self.wv.__getitem__` instead. - Refer to the documentation for :meth:`~gensim.models.keyedvectors.KeyedVectors.__getitem__`. - - """ - return self.wv.__getitem__(words) - - @deprecated("Method will be removed in 4.0.0, use self.wv.__contains__() instead") - def __contains__(self, word): - """Deprecated. Use `self.wv.__contains__` instead. - Refer to the documentation for :meth:`~gensim.models.keyedvectors.KeyedVectors.__contains__`. - - """ - return self.wv.__contains__(word) - def predict_output_word(self, context_words_list, topn=10): """Get the probability distribution of the center word given context words. @@ -982,15 +965,6 @@ def predict_output_word(self, context_words_list, topn=10): # returning the most probable output words with their probabilities return [(self.wv.index2word[index1], prob_values[index1]) for index1 in top_indices] - def init_sims(self, replace=False): - """Deprecated. Use `self.wv.init_sims` instead. - See :meth:`~gensim.models.keyedvectors.KeyedVectors.init_sims`. - - """ - if replace and hasattr(self.trainables, 'syn1'): - del self.trainables.syn1 - return self.wv.init_sims(replace) - def reset_from(self, other_model): """Borrow shareable pre-built structures from `other_model` and reset hidden layer weights. @@ -1014,23 +988,6 @@ def reset_from(self, other_model): self.corpus_count = other_model.corpus_count self.trainables.reset_weights(self.hs, self.negative, self.wv) - @staticmethod - def log_accuracy(section): - """Deprecated. Use `self.wv.log_accuracy` instead. - See :meth:`~gensim.models.word2vec.KeyedVectors.log_accuracy`. - - """ - return KeyedVectors.log_accuracy(section) - - @deprecated("Method will be removed in 4.0.0, use self.wv.evaluate_word_analogies() instead") - def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_insensitive=True): - """Deprecated. Use `self.wv.accuracy` instead. - See :meth:`~gensim.models.word2vec.KeyedVectors.accuracy`. - - """ - most_similar = most_similar or KeyedVectors.most_similar - return self.wv.accuracy(questions, restrict_vocab, most_similar, case_insensitive) - def __str__(self): """Human readable representation of the model's state. @@ -1045,24 +1002,6 @@ def __str__(self): self.__class__.__name__, len(self.wv.index2word), self.wv.vector_size, self.alpha ) - def delete_temporary_training_data(self, replace_word_vectors_with_normalized=False): - """Discard parameters that are used in training and scoring, to save memory. - - Warnings - -------- - Use only if you're sure you're done training a model. - - Parameters - ---------- - replace_word_vectors_with_normalized : bool, optional - If True, forget the original (not normalized) word vectors and only keep - the L2-normalized word vectors, to save even more memory. - - """ - if replace_word_vectors_with_normalized: - self.init_sims(replace=True) - self._minimize_model() - def save(self, *args, **kwargs): """Save the model. This saved model can be loaded again using :func:`~gensim.models.word2vec.Word2Vec.load`, which supports @@ -1089,34 +1028,6 @@ def get_latest_training_loss(self): """ return self.running_training_loss - @deprecated( - "Method will be removed in 4.0.0, keep just_word_vectors = model.wv to retain just the KeyedVectors instance" - ) - def _minimize_model(self, save_syn1=False, save_syn1neg=False, save_vectors_lockf=False): - if save_syn1 and save_syn1neg and save_vectors_lockf: - return - if hasattr(self.trainables, 'syn1') and not save_syn1: - del self.trainables.syn1 - if hasattr(self.trainables, 'syn1neg') and not save_syn1neg: - del self.trainables.syn1neg - if hasattr(self.trainables, 'vectors_lockf') and not save_vectors_lockf: - del self.trainables.vectors_lockf - self.model_trimmed_post_training = True - - @classmethod - def load_word2vec_format( - cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', - limit=None, datatype=REAL): - """Deprecated. Use :meth:`gensim.models.KeyedVectors.load_word2vec_format` instead.""" - raise DeprecationWarning("Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.") - - def save_word2vec_format(self, fname, fvocab=None, binary=False): - """Deprecated. Use `model.wv.save_word2vec_format` instead. - See :meth:`gensim.models.KeyedVectors.save_word2vec_format`. - - """ - raise DeprecationWarning("Deprecated. Use model.wv.save_word2vec_format instead.") - @classmethod def load(cls, *args, **kwargs): """Load a previously saved :class:`~gensim.models.word2vec.Word2Vec` model. diff --git a/gensim/similarities/docsim.py b/gensim/similarities/docsim.py index 256f276394..316b3a4c28 100755 --- a/gensim/similarities/docsim.py +++ b/gensim/similarities/docsim.py @@ -993,7 +993,7 @@ def __str__(self): class WmdSimilarity(interfaces.SimilarityABC): """Compute negative WMD similarity against a corpus of documents. - See :class:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors` for more information. + See :class:`~gensim.models.keyedvectors.KeyedVectors` for more information. Also, tutorial `notebook `_ for more examples. @@ -1052,7 +1052,7 @@ def __init__(self, corpus, w2v_model, num_best=None, normalize_w2v_and_replace=T if normalize_w2v_and_replace: # Normalize vectors in word2vec class to length 1. - w2v_model.init_sims(replace=True) + w2v_model.wv.init_sims(replace=True) def __len__(self): """Get size of corpus.""" diff --git a/gensim/similarities/index.py b/gensim/similarities/index.py index 08ecd221c6..392d000b4e 100644 --- a/gensim/similarities/index.py +++ b/gensim/similarities/index.py @@ -47,7 +47,6 @@ from gensim.models.word2vec import Word2Vec from gensim.models.fasttext import FastText from gensim.models import KeyedVectors -from gensim.models.keyedvectors import WordEmbeddingsKeyedVectors _NOANNOY = ImportError( @@ -97,7 +96,7 @@ def __init__(self, model=None, num_trees=None): self.build_from_doc2vec() elif isinstance(self.model, (Word2Vec, FastText)): self.build_from_word2vec() - elif isinstance(self.model, (WordEmbeddingsKeyedVectors, KeyedVectors)): + elif isinstance(self.model, (KeyedVectors,)): self.build_from_keyedvectors() else: raise ValueError("Only a Word2Vec, Doc2Vec, FastText or KeyedVectors instance can be used") diff --git a/gensim/similarities/nmslib.py b/gensim/similarities/nmslib.py index c2e23717d3..ecf8a9f132 100644 --- a/gensim/similarities/nmslib.py +++ b/gensim/similarities/nmslib.py @@ -79,7 +79,7 @@ from gensim.models.word2vec import Word2Vec from gensim.models.fasttext import FastText from gensim.models import KeyedVectors -from gensim.models.keyedvectors import WordEmbeddingsKeyedVectors + try: import nmslib except ImportError: @@ -129,7 +129,7 @@ def __init__(self, model, index_params=None, query_time_params=None): self._build_from_doc2vec() elif isinstance(self.model, (Word2Vec, FastText)): self._build_from_word2vec() - elif isinstance(self.model, (WordEmbeddingsKeyedVectors, KeyedVectors)): + elif isinstance(self.model, (KeyedVectors,)): self._build_from_keyedvectors() else: raise ValueError("model must be a Word2Vec, Doc2Vec, FastText or KeyedVectors instance") diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index d8b358f1fa..70e071cf51 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -625,40 +625,6 @@ def models_equal(self, model, model2): self.assertEqual(len(model.docvecs.doctags), len(model2.docvecs.doctags)) self.assertEqual(len(model.docvecs.offset2doctag), len(model2.docvecs.offset2doctag)) - def test_delete_temporary_training_data(self): - """Test doc2vec model after delete_temporary_training_data""" - for i in [0, 1]: - for j in [0, 1]: - model = doc2vec.Doc2Vec(sentences, vector_size=5, min_count=1, window=4, hs=i, negative=j) - if i: - self.assertTrue(hasattr(model.trainables, 'syn1')) - if j: - self.assertTrue(hasattr(model.trainables, 'syn1neg')) - self.assertTrue(hasattr(model, 'syn0_lockf')) - model.delete_temporary_training_data(keep_doctags_vectors=False, keep_inference=False) - self.assertTrue(len(model['human']), 10) - self.assertTrue(model.wv.vocab['graph'].count, 5) - self.assertTrue(not hasattr(model.trainables, 'syn1')) - self.assertTrue(not hasattr(model.trainables, 'syn1neg')) - self.assertTrue(not hasattr(model.trainables, 'syn0_lockf')) - self.assertTrue(model.docvecs and not hasattr(model.docvecs, 'vectors_docs')) - self.assertTrue(model.docvecs and not hasattr(model.docvecs, 'doctag_syn0_lockf')) - model = doc2vec.Doc2Vec( - list_corpus, dm=1, dm_mean=1, vector_size=24, window=4, hs=1, - negative=0, alpha=0.05, min_count=2, epochs=20 - ) - model.delete_temporary_training_data(keep_doctags_vectors=True, keep_inference=True) - self.assertTrue(model.docvecs and hasattr(model.docvecs, 'vectors_docs')) - self.assertTrue(hasattr(model.trainables, 'syn1')) - self.model_sanity(model, keep_training=False) - model = doc2vec.Doc2Vec( - list_corpus, dm=1, dm_mean=1, vector_size=24, window=4, hs=0, - negative=1, alpha=0.05, min_count=2, epochs=20 - ) - model.delete_temporary_training_data(keep_doctags_vectors=True, keep_inference=True) - self.model_sanity(model, keep_training=False) - self.assertTrue(hasattr(model.trainables, 'syn1neg')) - def test_word_vec_non_writeable(self): model = keyedvectors.KeyedVectors.load_word2vec_format(datapath('word2vec_pre_kv_c')) vector = model['says'] diff --git a/gensim/test/test_poincare.py b/gensim/test/test_poincare.py index 9ea020da51..c4fe8af433 100644 --- a/gensim/test/test_poincare.py +++ b/gensim/test/test_poincare.py @@ -59,7 +59,7 @@ def setUp(self): def models_equal(self, model_1, model_2): self.assertEqual(len(model_1.kv.vocab), len(model_2.kv.vocab)) self.assertEqual(set(model_1.kv.vocab.keys()), set(model_2.kv.vocab.keys())) - self.assertTrue(np.allclose(model_1.kv.syn0, model_2.kv.syn0)) + self.assertTrue(np.allclose(model_1.kv.vectors, model_2.kv.vectors)) def test_data_counts(self): """Tests whether data has been loaded correctly and completely.""" @@ -116,7 +116,7 @@ def test_train_after_load(self): def test_persistence_old_model(self): """Tests whether model from older gensim version is loaded correctly.""" loaded = PoincareModel.load(datapath('poincare_test_3.4.0')) - self.assertEqual(loaded.kv.syn0.shape, (239, 2)) + self.assertEqual(loaded.kv.vectors.shape, (239, 2)) self.assertEqual(len(loaded.kv.vocab), 239) self.assertEqual(loaded.size, 2) self.assertEqual(len(loaded.all_relations), 200) @@ -124,9 +124,9 @@ def test_persistence_old_model(self): def test_train_old_model_after_load(self): """Tests whether loaded model from older gensim version can be trained correctly.""" loaded = PoincareModel.load(datapath('poincare_test_3.4.0')) - old_vectors = np.copy(loaded.kv.syn0) + old_vectors = np.copy(loaded.kv.vectors) loaded.train(epochs=2) - self.assertFalse(np.allclose(old_vectors, loaded.kv.syn0)) + self.assertFalse(np.allclose(old_vectors, loaded.kv.vectors)) def test_invalid_data_raises_error(self): """Tests that error is raised on invalid input data.""" @@ -140,34 +140,34 @@ def test_invalid_data_raises_error(self): def test_vector_shape(self): """Tests whether vectors are initialized with the correct size.""" model = PoincareModel(self.data, size=20) - self.assertEqual(model.kv.syn0.shape, (7, 20)) + self.assertEqual(model.kv.vectors.shape, (7, 20)) def test_vector_dtype(self): """Tests whether vectors have the correct dtype before and after training.""" model = PoincareModel(self.data_large, dtype=np.float32, burn_in=0, negative=3) - self.assertEqual(model.kv.syn0.dtype, np.float32) + self.assertEqual(model.kv.vectors.dtype, np.float32) model.train(epochs=1) - self.assertEqual(model.kv.syn0.dtype, np.float32) + self.assertEqual(model.kv.vectors.dtype, np.float32) def test_training(self): """Tests that vectors are different before and after training.""" model = PoincareModel(self.data_large, burn_in=0, negative=3) - old_vectors = np.copy(model.kv.syn0) + old_vectors = np.copy(model.kv.vectors) model.train(epochs=2) - self.assertFalse(np.allclose(old_vectors, model.kv.syn0)) + self.assertFalse(np.allclose(old_vectors, model.kv.vectors)) def test_training_multiple(self): """Tests that calling train multiple times results in different vectors.""" model = PoincareModel(self.data_large, burn_in=0, negative=3) model.train(epochs=2) - old_vectors = np.copy(model.kv.syn0) + old_vectors = np.copy(model.kv.vectors) model.train(epochs=1) - self.assertFalse(np.allclose(old_vectors, model.kv.syn0)) + self.assertFalse(np.allclose(old_vectors, model.kv.vectors)) - old_vectors = np.copy(model.kv.syn0) + old_vectors = np.copy(model.kv.vectors) model.train(epochs=0) - self.assertTrue(np.allclose(old_vectors, model.kv.syn0)) + self.assertTrue(np.allclose(old_vectors, model.kv.vectors)) def test_gradients_check(self): """Tests that the model is trained successfully with gradients check enabled.""" @@ -192,22 +192,22 @@ def test_reproducible(self): model_2 = PoincareModel(self.data_large, seed=1, negative=3, burn_in=1) model_2.train(epochs=2) - self.assertTrue(np.allclose(model_1.kv.syn0, model_2.kv.syn0)) + self.assertTrue(np.allclose(model_1.kv.vectors, model_2.kv.vectors)) def test_burn_in(self): """Tests that vectors are different after burn-in.""" model = PoincareModel(self.data, burn_in=1, negative=3) - original_vectors = np.copy(model.kv.syn0) + original_vectors = np.copy(model.kv.vectors) model.train(epochs=0) - self.assertFalse(np.allclose(model.kv.syn0, original_vectors)) + self.assertFalse(np.allclose(model.kv.vectors, original_vectors)) def test_burn_in_only_done_once(self): """Tests that burn-in does not happen when train is called a second time.""" model = PoincareModel(self.data, negative=3, burn_in=1) model.train(epochs=0) - original_vectors = np.copy(model.kv.syn0) + original_vectors = np.copy(model.kv.vectors) model.train(epochs=0) - self.assertTrue(np.allclose(model.kv.syn0, original_vectors)) + self.assertTrue(np.allclose(model.kv.vectors, original_vectors)) def test_negatives(self): """Tests that correct number of negatives are sampled.""" diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index ef176754da..db24fdacd1 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -342,11 +342,11 @@ def rule(word, count, min_count): model = word2vec.Word2Vec(sentences, min_count=1, trim_rule=rule) self.assertTrue("human" not in model.wv.vocab) - def testSyn0NormNotSaved(self): - """Test syn0norm isn't saved in model file""" + def testVectorsNormNotSaved(self): + """Test vectors_norm isn't saved in model file""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() model.save(tmpf) loaded_model = word2vec.Word2Vec.load(tmpf) self.assertTrue(loaded_model.wv.vectors_norm is None) @@ -387,7 +387,7 @@ def testPersistenceWord2VecFormat(self): """Test storing/loading the entire model in word2vec format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() model.wv.save_word2vec_format(tmpf, binary=True) binary_model_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) binary_model_kv.init_sims(replace=False) @@ -406,7 +406,7 @@ def testPersistenceWord2VecFormat(self): def testNoTrainingCFormat(self): tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() model.wv.save_word2vec_format(tmpf, binary=True) kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) binary_model = word2vec.Word2Vec() @@ -416,7 +416,7 @@ def testNoTrainingCFormat(self): def testTooShortBinaryWord2VecFormat(self): tfile = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() model.wv.save_word2vec_format(tfile, binary=True) f = open(tfile, 'r+b') f.write(b'13') # write wrong (too-long) vector count @@ -426,7 +426,7 @@ def testTooShortBinaryWord2VecFormat(self): def testTooShortTextWord2VecFormat(self): tfile = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() model.wv.save_word2vec_format(tfile, binary=False) f = open(tfile, 'r+b') f.write(b'13') # write wrong (too-long) vector count @@ -437,7 +437,7 @@ def testPersistenceWord2VecFormatNonBinary(self): """Test storing/loading the entire model in word2vec non-binary format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() model.wv.save_word2vec_format(tmpf, binary=False) text_model = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=False) text_model.init_sims(False) @@ -453,7 +453,7 @@ def testPersistenceWord2VecFormatWithVocab(self): """Test storing/loading the entire model and vocabulary in word2vec format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) binary_model_with_vocab_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) @@ -463,7 +463,7 @@ def testPersistenceKeyedVectorsFormatWithVocab(self): """Test storing/loading the entire model and vocabulary in word2vec format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) kv_binary_model_with_vocab = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) @@ -475,7 +475,7 @@ def testPersistenceWord2VecFormatCombinationWithStandardPersistence(self): It was possible prior to 1.0.0 release, now raises Exception""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.init_sims() + model.wv.init_sims() testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) binary_model_with_vocab_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) @@ -839,32 +839,6 @@ def models_equal(self, model, model2): most_common_word = max(model.wv.vocab.items(), key=lambda item: item[1].count)[0] self.assertTrue(np.allclose(model.wv[most_common_word], model2.wv[most_common_word])) - def testDeleteTemporaryTrainingData(self): - """Test word2vec model after delete_temporary_training_data""" - for i in [0, 1]: - for j in [0, 1]: - model = word2vec.Word2Vec(sentences, size=10, min_count=0, seed=42, hs=i, negative=j) - if i: - self.assertTrue(hasattr(model.trainables, 'syn1')) - if j: - self.assertTrue(hasattr(model, 'syn1neg')) - self.assertTrue(hasattr(model, 'syn0_lockf')) - model.delete_temporary_training_data(replace_word_vectors_with_normalized=True) - self.assertTrue(len(model.wv['human']), 10) - self.assertTrue(len(model.wv.vocab), 12) - self.assertTrue(model.wv.vocab['graph'].count, 3) - self.assertTrue(not hasattr(model.trainables, 'syn1')) - self.assertTrue(not hasattr(model.trainables, 'syn1neg')) - self.assertTrue(not hasattr(model.trainables, 'syn0_lockf')) - - def testNormalizeAfterTrainingData(self): - tmpf = get_tmpfile('gensim_word2vec.tst') - model = word2vec.Word2Vec(sentences, min_count=1) - model.save(tmpf) - norm_only_model = word2vec.Word2Vec.load(tmpf) - norm_only_model.delete_temporary_training_data(replace_word_vectors_with_normalized=True) - self.assertFalse(np.allclose(model.wv['human'], norm_only_model.wv['human'])) - def testPredictOutputWord(self): '''Test word2vec predict_output_word method handling for negative sampling scheme''' # under normal circumstances @@ -878,7 +852,7 @@ def testPredictOutputWord(self): # when required model parameters have been deleted tmpf = get_tmpfile('gensim_word2vec.tst') - model_with_neg.init_sims() + model_with_neg.wv.init_sims() model_with_neg.wv.save_word2vec_format(tmpf, binary=True) kv_model_with_neg = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) binary_model_with_neg = word2vec.Word2Vec() From 4e334c18e08362d70a18e484d68b486fda308430 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Sat, 7 Dec 2019 12:22:52 -0800 Subject: [PATCH 05/60] update usages, tests, flake8 cleanup --- gensim/models/doc2vec.py | 5 ++--- gensim/models/fasttext.py | 29 +++++++---------------------- gensim/models/keyedvectors.py | 14 +++++++------- gensim/models/poincare.py | 3 +-- gensim/models/word2vec.py | 32 ++++++++++++++++---------------- gensim/similarities/nmslib.py | 2 +- gensim/test/test_fasttext.py | 5 +++-- gensim/test/test_similarities.py | 32 ++++++++++++++++---------------- gensim/test/test_utils.py | 1 - 9 files changed, 53 insertions(+), 70 deletions(-) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 5cf392b67f..19af97b560 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -63,7 +63,6 @@ import logging import os -import warnings try: from queue import Queue @@ -77,7 +76,7 @@ from numpy import zeros, float32 as REAL, empty, ones, \ memmap as np_memmap, vstack, integer, dtype -from gensim.utils import call_on_class_only, deprecated +from gensim.utils import call_on_class_only from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc from gensim.models.word2vec import KeyedVectors, Word2VecVocab, Word2VecTrainables from gensim.models.word2vec import train_cbow_pair, train_sg_pair, train_batch_sg # noqa @@ -376,7 +375,7 @@ def reset_from(self, other_model): """ self.wv.vocab = other_model.wv.vocab - self.wv.index2word = other_model.wv.index2word + self.wv.index2key = other_model.wv.index2key self.vocabulary.cum_table = other_model.vocabulary.cum_table self.corpus_count = other_model.corpus_count self.docvecs.count = other_model.docvecs.count diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 79035323fc..e13651433a 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -289,7 +289,7 @@ import gensim.models._fasttext_bin from gensim.models.word2vec import Word2VecVocab, Word2VecTrainables -from gensim.models.keyedvectors import KeyedVectors, _save_word2vec_format +from gensim.models.keyedvectors import KeyedVectors from gensim.models.base_any2vec import BaseWordEmbeddingsModel from gensim.utils import deprecated, call_on_class_only, open, NO_CYTHON @@ -858,6 +858,12 @@ def load(cls, *args, **kwargs): if not hasattr(model.trainables, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'): model.trainables.vectors_ngrams_lockf = ones(len(model.wv.vectors_ngrams), dtype=REAL) + # fixup mistakenly overdimensioned gensim-3.x lockf arrays + if len(model.trainables.vectors_vocab_lockf.shape) > 1: + model.trainables.vectors_vocab_lockf = model.trainables.vectors_vocab_lockf[:, 0] + if len(model.trainables.vectors_ngrams_lockf.shape) > 1: + model.trainables.vectors_ngrams_lockf = model.trainables.vectors_ngrams_lockf[:, 0] + if not hasattr(model.wv, 'bucket'): model.wv.bucket = model.trainables.bucket except AttributeError: @@ -1419,27 +1425,6 @@ def get_vector(self, word, use_norm=False): else: return word_vec - 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) - def init_ngrams_weights(self, seed): """Initialize the vocabulary and ngrams weights prior to training. diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index ef93e01ef0..0d500d752c 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -297,7 +297,7 @@ def add(self, entities, weights, replace=False): 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) + self.index2key.append(entity) # add vectors for new entities self.vectors = vstack((self.vectors, weights[~in_vocab_mask].astype(self.vectors.dtype))) @@ -339,7 +339,7 @@ def closer_than(self, entity1, entity2): 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] + return [self.index2key[index] for index in closer_node_indices if index != e1_index] def words_closer_than(self, word1, word2): return self.closer_than(word1, word2) @@ -461,7 +461,7 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non 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] + result = [(self.index2key[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): @@ -691,7 +691,7 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): 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] + result = [(self.index2key[sim], float(dists[sim])) for sim in best if sim not in all_words] return result[:topn] def doesnt_match(self, words): @@ -899,7 +899,7 @@ def evaluate_word_analogies(self, analogies, restrict_vocab=300000, case_insensi keys 'correct' and 'incorrect'. """ - ok_vocab = [(w, self.vocab[w]) for w in self.index2word[:restrict_vocab]] + ok_vocab = [(w, self.vocab[w]) for w in self.index2key[: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) @@ -1031,7 +1031,7 @@ def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, The ratio of pairs with unknown words. """ - ok_vocab = [(w, self.vocab[w]) for w in self.index2word[:restrict_vocab]] + ok_vocab = [(w, self.vocab[w]) for w in self.index2key[:restrict_vocab]] ok_vocab = {w.upper(): v for w, v in reversed(ok_vocab)} if case_insensitive else dict(ok_vocab) similarity_gold = [] @@ -1731,7 +1731,7 @@ def _add_word_to_result(result, counts, word, weights, vocab_size): result.vocab[word] = Vocab(index=word_id, count=word_count) result.vectors[word_id] = weights - result.index2word.append(word) + result.index2key.append(word) def _add_bytes_to_result(result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors): diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index 906c23c27c..42b7a3d802 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -56,8 +56,7 @@ from six.moves import zip, range from gensim import utils, matutils -from gensim.models.keyedvectors import Vocab, KeyedVectors, _save_word2vec_format, _load_word2vec_format -from numpy import float32 as REAL +from gensim.models.keyedvectors import Vocab, KeyedVectors try: from autograd import grad # Only required for optionally verifying gradients while training diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 758c4380f0..20204cdc86 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -963,7 +963,7 @@ def predict_output_word(self, context_words_list, topn=10): prob_values /= sum(prob_values) top_indices = matutils.argsort(prob_values, topn=topn, reverse=True) # returning the most probable output words with their probabilities - return [(self.wv.index2word[index1], prob_values[index1]) for index1 in top_indices] + return [(self.wv.index2key[index1], prob_values[index1]) for index1 in top_indices] def reset_from(self, other_model): """Borrow shareable pre-built structures from `other_model` and reset hidden layer weights. @@ -983,7 +983,7 @@ def reset_from(self, other_model): """ self.wv.vocab = other_model.wv.vocab - self.wv.index2word = other_model.wv.index2word + self.wv.index2key = other_model.wv.index2key self.vocabulary.cum_table = other_model.vocabulary.cum_table self.corpus_count = other_model.corpus_count self.trainables.reset_weights(self.hs, self.negative, self.wv) @@ -999,7 +999,7 @@ def __str__(self): """ return "%s(vocab=%s, size=%s, alpha=%s)" % ( - self.__class__.__name__, len(self.wv.index2word), self.wv.vector_size, self.alpha + self.__class__.__name__, len(self.wv.index2key), self.wv.vector_size, self.alpha ) def save(self, *args, **kwargs): @@ -1324,8 +1324,8 @@ def sort_vocab(self, wv): """Sort the vocabulary so the most frequent words have the lowest indexes.""" if len(wv.vectors): raise RuntimeError("cannot sort vocabulary after model weights already initialized.") - wv.index2word.sort(key=lambda word: wv.vocab[word].count, reverse=True) - for i, word in enumerate(wv.index2word): + wv.index2key.sort(key=lambda word: wv.vocab[word].count, reverse=True) + for i, word in enumerate(wv.index2key): wv.vocab[word].index = i def prepare_vocab( @@ -1370,7 +1370,7 @@ def prepare_vocab( retain_total, retain_words = 0, [] # Discard words less-frequent than min_count if not dry_run: - wv.index2word = [] + wv.index2key = [] # make stored settings match these applied settings self.min_count = min_count self.sample = sample @@ -1381,8 +1381,8 @@ def prepare_vocab( retain_words.append(word) retain_total += v if not dry_run: - wv.vocab[word] = Vocab(count=v, index=len(wv.index2word)) - wv.index2word.append(word) + wv.vocab[word] = Vocab(count=v, index=len(wv.index2key)) + wv.index2key.append(word) else: drop_unique += 1 drop_total += v @@ -1413,8 +1413,8 @@ def prepare_vocab( new_words.append(word) new_total += v if not dry_run: - wv.vocab[word] = Vocab(count=v, index=len(wv.index2word)) - wv.index2word.append(word) + wv.vocab[word] = Vocab(count=v, index=len(wv.index2key)) + wv.index2key.append(word) else: drop_unique += 1 drop_total += v @@ -1489,7 +1489,7 @@ def prepare_vocab( def add_null_word(self, wv): word, v = '\0', Vocab(count=1, sample_int=0) v.index = len(wv.vocab) - wv.index2word.append(word) + wv.index2key.append(word) wv.vocab[word] = v def create_binary_tree(self, wv): @@ -1511,15 +1511,15 @@ def make_cum_table(self, wv, domain=2**31 - 1): Called internally from :meth:`~gensim.models.word2vec.Word2VecVocab.build_vocab`. """ - vocab_size = len(wv.index2word) + vocab_size = len(wv.index2key) self.cum_table = zeros(vocab_size, dtype=uint32) # compute sum of all power (Z in paper) train_words_pow = 0.0 for word_index in range(vocab_size): - train_words_pow += wv.vocab[wv.index2word[word_index]].count**self.ns_exponent + train_words_pow += wv.vocab[wv.index2key[word_index]].count**self.ns_exponent cumulative = 0.0 for word_index in range(vocab_size): - cumulative += wv.vocab[wv.index2word[word_index]].count**self.ns_exponent + cumulative += wv.vocab[wv.index2key[word_index]].count**self.ns_exponent self.cum_table[word_index] = round(cumulative / train_words_pow * domain) if len(self.cum_table) > 0: assert self.cum_table[-1] == domain @@ -1612,7 +1612,7 @@ def reset_weights(self, hs, negative, wv): # randomize weights vector by vector, rather than materializing a huge random matrix in RAM at once for i in range(len(wv.vocab)): # construct deterministic seed from word AND seed argument - wv.vectors[i] = self.seeded_vector(wv.index2word[i] + str(self.seed), wv.vector_size) + wv.vectors[i] = self.seeded_vector(wv.index2key[i] + str(self.seed), wv.vector_size) if hs: self.syn1 = zeros((len(wv.vocab), self.layer1_size), dtype=REAL) if negative: @@ -1630,7 +1630,7 @@ def update_weights(self, hs, negative, wv): # randomize the remaining words for i in range(len(wv.vectors), len(wv.vocab)): # construct deterministic seed from word AND seed argument - newvectors[i - len(wv.vectors)] = self.seeded_vector(wv.index2word[i] + str(self.seed), wv.vector_size) + newvectors[i - len(wv.vectors)] = self.seeded_vector(wv.index2key[i] + str(self.seed), wv.vector_size) # Raise an error if an online update is run before initial training on a corpus if not len(wv.vectors): diff --git a/gensim/similarities/nmslib.py b/gensim/similarities/nmslib.py index ecf8a9f132..77fa1fdd74 100644 --- a/gensim/similarities/nmslib.py +++ b/gensim/similarities/nmslib.py @@ -181,7 +181,7 @@ def load(cls, fname): def _build_from_word2vec(self): """Build an NMSLIB index using word vectors from a Word2Vec model.""" - self.model.init_sims() + self.model.wv.init_sims() self._build_from_model(self.model.wv.vectors_norm, self.model.wv.index2word) def _build_from_doc2vec(self): diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index a2f7aa3866..bf602deda1 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -23,6 +23,7 @@ from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences import gensim.models._fasttext_bin from gensim.models.fasttext_inner import compute_ngrams, compute_ngrams_bytes, ft_hash_broken, ft_hash_bytes +from gensim.models.fasttext import _unpack, _unpack_copy import gensim.models.fasttext @@ -1174,7 +1175,7 @@ def test_hash_native(self): self.assertEqual(m.trainables.bucket, m.wv.bucket) -class HashTest(unittest.TestCase): +class FTHashResultsTest(unittest.TestCase): """Loosely based on the test described here: https://github.com/RaRe-Technologies/gensim/issues/2059#issuecomment-432300777 @@ -1238,7 +1239,7 @@ def hash_main(alg): print('u%r: %r,' % (word, fun(word))) -class HashTest(unittest.TestCase): +class FTHashFunctionsTest(unittest.TestCase): def setUp(self): # # I obtained these expected values using: diff --git a/gensim/test/test_similarities.py b/gensim/test/test_similarities.py index 77089a6f02..4d049350e5 100644 --- a/gensim/test/test_similarities.py +++ b/gensim/test/test_similarities.py @@ -553,11 +553,11 @@ def setUp(self): def testWord2Vec(self): model = word2vec.Word2Vec(texts, min_count=1) - model.init_sims() + model.wv.init_sims() index = self.indexer(model, 10) self.assertVectorIsSimilarToItself(model.wv, index) - self.assertApproxNeighborsMatchExact(model, model.wv, index) + self.assertApproxNeighborsMatchExact(model.wv, model.wv, index) self.assertIndexSaved(index) self.assertLoadedIndexEqual(index, model) @@ -572,11 +572,11 @@ def __iter__(self): yield line.lower().strip().split() model = FastText(LeeReader(datapath('lee.cor')), bucket=5000) - model.init_sims() + model.wv.init_sims() index = self.indexer(model, 10) self.assertVectorIsSimilarToItself(model.wv, index) - self.assertApproxNeighborsMatchExact(model, model.wv, index) + self.assertApproxNeighborsMatchExact(model.wv, model.wv, index) self.assertIndexSaved(index) self.assertLoadedIndexEqual(index, model) @@ -607,8 +607,8 @@ def assertVectorIsSimilarToItself(self, wv, index): def assertApproxNeighborsMatchExact(self, model, wv, index): vector = wv.vectors_norm[0] - approx_neighbors = model.wv.most_similar([vector], topn=5, indexer=index) - exact_neighbors = model.wv.most_similar(positive=[vector], topn=5) + approx_neighbors = model.most_similar([vector], topn=5, indexer=index) + exact_neighbors = model.most_similar(positive=[vector], topn=5) approx_words = [neighbor[0] for neighbor in approx_neighbors] exact_words = [neighbor[0] for neighbor in exact_neighbors] @@ -617,8 +617,8 @@ def assertApproxNeighborsMatchExact(self, model, wv, index): def assertAllSimilaritiesDisableIndexer(self, model, wv, index): vector = wv.vectors_norm[0] - approx_similarities = model.wv.most_similar([vector], topn=None, indexer=index) - exact_similarities = model.wv.most_similar(positive=[vector], topn=None) + approx_similarities = model.most_similar([vector], topn=None, indexer=index) + exact_similarities = model.most_similar(positive=[vector], topn=None) self.assertEqual(approx_similarities, exact_similarities) self.assertEqual(len(approx_similarities), len(wv.vectors.vocab)) @@ -655,7 +655,7 @@ def setUp(self): from gensim.similarities.index import AnnoyIndexer self.model = doc2vec.Doc2Vec(sentences, min_count=1) - self.model.init_sims() + self.model.docvecs.init_sims() self.index = AnnoyIndexer(self.model, 300) self.vector = self.model.docvecs.vectors_docs_norm[0] @@ -716,11 +716,11 @@ def setUp(self): def test_word2vec(self): model = word2vec.Word2Vec(texts, min_count=1) - model.init_sims() + model.wv.init_sims() index = self.indexer(model) self.assertVectorIsSimilarToItself(model.wv, index) - self.assertApproxNeighborsMatchExact(model, model.wv, index) + self.assertApproxNeighborsMatchExact(model.wv, model.wv, index) self.assertIndexSaved(index) self.assertLoadedIndexEqual(index, model) @@ -735,11 +735,11 @@ def __iter__(self): yield line.lower().strip().split() model = FastText(LeeReader(datapath('lee.cor')), bucket=5000) - model.init_sims() + model.wv.init_sims() index = self.indexer(model) self.assertVectorIsSimilarToItself(model.wv, index) - self.assertApproxNeighborsMatchExact(model, model.wv, index) + self.assertApproxNeighborsMatchExact(model.wv, model.wv, index) self.assertIndexSaved(index) self.assertLoadedIndexEqual(index, model) @@ -768,8 +768,8 @@ def assertVectorIsSimilarToItself(self, wv, index): def assertApproxNeighborsMatchExact(self, model, wv, index): vector = wv.vectors_norm[0] - approx_neighbors = model.wv.most_similar([vector], topn=5, indexer=index) - exact_neighbors = model.wv.most_similar(positive=[vector], topn=5) + approx_neighbors = model.most_similar([vector], topn=5, indexer=index) + exact_neighbors = model.most_similar(positive=[vector], topn=5) approx_words = [neighbor[0] for neighbor in approx_neighbors] exact_words = [neighbor[0] for neighbor in exact_neighbors] @@ -807,7 +807,7 @@ def setUp(self): from gensim.similarities.nmslib import NmslibIndexer self.model = doc2vec.Doc2Vec(sentences, min_count=1) - self.model.init_sims() + self.model.docvecs.init_sims() self.index = NmslibIndexer(self.model) self.vector = self.model.docvecs.vectors_docs_norm[0] diff --git a/gensim/test/test_utils.py b/gensim/test/test_utils.py index 424bdc62f7..626c0de06c 100644 --- a/gensim/test/test_utils.py +++ b/gensim/test/test_utils.py @@ -8,7 +8,6 @@ """ from __future__ import unicode_literals -import sys import logging import unittest From a16cec598e1b98269eff5029e416b305fb1031cc Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 12 Dec 2019 14:18:49 -0800 Subject: [PATCH 06/60] expand KeyedVectors to obviate Doc2VecKeyedVectors; upconvert old offset-style doctags --- gensim/models/deprecated/doc2vec.py | 2 + gensim/models/doc2vec.py | 208 ++++---- gensim/models/keyedvectors.py | 679 ++++++++----------------- gensim/models/word2vec.py | 34 +- gensim/test/test_doc2vec.py | 57 +-- gensim/test/test_translation_matrix.py | 6 + 6 files changed, 339 insertions(+), 647 deletions(-) diff --git a/gensim/models/deprecated/doc2vec.py b/gensim/models/deprecated/doc2vec.py index 9378b77d88..41f74fdc6b 100644 --- a/gensim/models/deprecated/doc2vec.py +++ b/gensim/models/deprecated/doc2vec.py @@ -156,6 +156,8 @@ def load_old_doc2vec(*args, **kwargs): # 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 + # now upconvert that to gensim-4.0.0+ + new_model.docvecs._upconvert_old_d2vkv() new_model.train_count = old_model.__dict__.get('train_count', None) new_model.corpus_count = old_model.__dict__.get('corpus_count', None) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 19af97b560..5045d08aa4 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -73,17 +73,18 @@ from collections.abc import Iterable from timeit import default_timer -from numpy import zeros, float32 as REAL, empty, ones, \ +from numpy import zeros, float32 as REAL, ones, \ memmap as np_memmap, vstack, integer, dtype +import numpy as np from gensim.utils import call_on_class_only from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc -from gensim.models.word2vec import KeyedVectors, Word2VecVocab, Word2VecTrainables +from gensim.models.word2vec import Word2VecVocab, Word2VecTrainables from gensim.models.word2vec import train_cbow_pair, train_sg_pair, train_batch_sg # noqa from six.moves import range from six import string_types, integer_types, itervalues from gensim.models.base_any2vec import BaseWordEmbeddingsModel -from gensim.models.keyedvectors import Doc2VecKeyedVectors +from gensim.models.keyedvectors import KeyedVectors, ConcatList, pseudorandom_weak_vector from types import GeneratorType logger = logging.getLogger(__name__) @@ -145,27 +146,21 @@ def __str__(self): return '%s(%s, %s)' % (self.__class__.__name__, self.words, self.tags) -class Doctag(namedtuple('Doctag', 'offset, word_count, doc_count')): +class Doctag(namedtuple('Doctag', 'index, word_count, doc_count')): """A string document tag discovered during the initial vocabulary scan. - The document-vector equivalent of a Vocab object. + The document-vector equivalent of a Vocab object. TODO: merge with Vocab 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 :class:`~gensim.models.doc2vec.Doctag` vectors begin at index - `(max_rawint + 1)`, so the true index is `(rawint_index + 1 + offset)`. - - See Also - -------- - :meth:`~gensim.models.keyedvectors.Doc2VecKeyedVectors._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) + @property + def count(self): + return self.doc_count + class Doc2Vec(BaseWordEmbeddingsModel): """Class for training, using and evaluating neural networks described in @@ -179,7 +174,7 @@ class Doc2Vec(BaseWordEmbeddingsModel): This object essentially contains the mapping between words and embeddings. After training, it can be used directly to query those embeddings in various ways. See the module level docstring for examples. - docvecs : :class:`~gensim.models.keyedvectors.Doc2VecKeyedVectors` + docvecs : :class:`~gensim.models.keyedvectors.KeyedVectors` This object contains the paragraph vectors learned from the training data. There will be one such vector for each unique document tag supplied during training. They may be individually accessed using the tag as an indexed-access key. For example, if one of the training documents used a tag of 'doc003': @@ -321,7 +316,7 @@ def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_wo vector_size=self.vector_size, **trainables_kwargs) self.wv = KeyedVectors(self.vector_size) - self.docvecs = docvecs or Doc2VecKeyedVectors(self.vector_size, docvecs_mapfile) + self.docvecs = docvecs or KeyedVectors(self.vector_size, mapfile_path=docvecs_mapfile) self.comment = comment @@ -363,7 +358,7 @@ def _clear_post_train(self): def clear_sims(self): """Resets the current word vectors. """ self.wv.vectors_norm = None - self.wv.vectors_docs_norm = None + self.docvecs.vectors_norm = None def reset_from(self, other_model): """Copy shareable data structures from another (possibly pre-trained) model. @@ -378,15 +373,14 @@ def reset_from(self, other_model): self.wv.index2key = other_model.wv.index2key self.vocabulary.cum_table = other_model.vocabulary.cum_table self.corpus_count = other_model.corpus_count - self.docvecs.count = other_model.docvecs.count - self.docvecs.doctags = other_model.docvecs.doctags - self.docvecs.offset2doctag = other_model.docvecs.offset2doctag + self.docvecs.vocab = other_model.docvecs.vocab + self.docvecs.index2key = other_model.docvecs.index2key self.trainables.reset_weights(self.hs, self.negative, self.wv, self.docvecs) def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, total_examples=None, total_words=None, offsets=None, start_doctags=None, **kwargs): work, neu1 = thread_private_mem - doctag_vectors = self.docvecs.vectors_docs + doctag_vectors = self.docvecs.vectors doctag_locks = self.trainables.vectors_docs_lockf offset = offsets[thread_id] @@ -395,17 +389,17 @@ def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_p if self.sg: examples, tally, raw_tally = d2v_train_epoch_dbow( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, - total_examples, total_words, work, neu1, self.docvecs.count, + total_examples, total_words, work, neu1, len(self.docvecs), doctag_vectors=doctag_vectors, doctag_locks=doctag_locks, train_words=self.dbow_words) elif self.dm_concat: examples, tally, raw_tally = d2v_train_epoch_dm_concat( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, - total_examples, total_words, work, neu1, self.docvecs.count, + total_examples, total_words, work, neu1, len(self.docvecs), doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) else: examples, tally, raw_tally = d2v_train_epoch_dm( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, - total_examples, total_words, work, neu1, self.docvecs.count, + total_examples, total_words, work, neu1, len(self.docvecs), doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) return examples, tally, raw_tally @@ -431,8 +425,8 @@ def _do_train_job(self, job, alpha, inits): work, neu1 = inits tally = 0 for doc in job: - doctag_indexes = self.vocabulary.indexed_doctags(doc.tags, self.docvecs) - doctag_vectors = self.docvecs.vectors_docs + doctag_indexes = [self.docvecs.get_index(tag) for tag in doc.tags if tag in self.docvecs] + doctag_vectors = self.docvecs.vectors doctag_locks = self.trainables.vectors_docs_lockf if self.sg: tally += train_document_dbow( @@ -600,7 +594,7 @@ def estimated_lookup_memory(self): The estimated RAM required to look up a tag in bytes. """ - return 60 * len(self.docvecs.offset2doctag) + 140 * len(self.docvecs.doctags) + return 60 * len(self.docvecs.vocab) + 140 * len(self.docvecs.vocab) def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps=None): """Infer a vector for given post-bulk training document. @@ -637,7 +631,10 @@ def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps min_alpha = min_alpha or self.min_alpha epochs = epochs or self.epochs - doctag_vectors, doctag_locks = self.trainables.get_doctag_trainables(doc_words, self.docvecs.vector_size) + doctag_vectors = pseudorandom_weak_vector(self.docvecs.vector_size, seed_string=' '.join(doc_words)) + doctag_vectors = doctag_vectors.reshape(1, self.docvecs.vector_size) + + doctag_locks = np.ones(1, dtype=REAL) doctag_indexes = [0] work = zeros(self.trainables.layer1_size, dtype=REAL) if not self.sg: @@ -746,21 +743,23 @@ def save_word2vec_format(self, fname, doctag_vec=False, word_vec=True, prefix='* If True, the data will be saved in binary word2vec format, otherwise - will be saved in plain text. """ - total_vec = len(self.wv.vocab) + len(self.docvecs) - write_first_line = False + total_vec = None # save word vectors if word_vec: - if not doctag_vec: - total_vec = len(self.wv.vocab) + if doctag_vec: + total_vec = len(self.wv) + len(self.docvecs) self.wv.save_word2vec_format(fname, fvocab, binary, total_vec) # save document vectors if doctag_vec: - if not word_vec: - total_vec = len(self.docvecs) - write_first_line = True + write_first_line = True + append = False + if word_vec: + # simply appending to existing file + write_first_line = False + append = True self.docvecs.save_word2vec_format( - fname, prefix=prefix, fvocab=fvocab, total_vec=total_vec, - binary=binary, write_first_line=write_first_line) + fname, prefix=prefix, fvocab=fvocab, binary=binary, + write_first_line=write_first_line, append=append) def init_sims(self, replace=False): """Pre-compute L2-normalized vectors. @@ -826,7 +825,7 @@ def estimate_memory(self, vocab_size=None, report=None): """ report = report or {} report['doctag_lookup'] = self.estimated_lookup_memory() - report['doctag_syn0'] = self.docvecs.count * self.vector_size * dtype(REAL).itemsize + report['doctag_syn0'] = len(self.docvecs) * self.vector_size * dtype(REAL).itemsize return super(Doc2Vec, self).estimate_memory(vocab_size, report=report) def build_vocab(self, documents=None, corpus_file=None, update=False, progress_per=10000, keep_raw_vocab=False, @@ -935,18 +934,33 @@ def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=No self.trainables.prepare_weights( self.hs, self.negative, self.wv, self.docvecs, update=update) + def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=None, steps=None): + """Compute cosine similarity between two post-bulk out of training documents. -def _note_doctag(key, document_length, docvecs): - """Note a document tag during initial corpus scan, for structure sizing.""" - if isinstance(key, integer_types + (integer,)): - docvecs.max_rawint = max(docvecs.max_rawint, key) - else: - if key in docvecs.doctags: - docvecs.doctags[key] = docvecs.doctags[key].repeat(document_length) - else: - docvecs.doctags[key] = Doctag(len(docvecs.offset2doctag), document_length, 1) - docvecs.offset2doctag.append(key) - docvecs.count = docvecs.max_rawint + 1 + len(docvecs.offset2doctag) + 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 = self.infer_vector(doc_words=doc_words1, alpha=alpha, min_alpha=min_alpha, steps=steps) + d2 = self.infer_vector(doc_words=doc_words2, alpha=alpha, min_alpha=min_alpha, steps=steps) + return np.dot(matutils.unitvec(d1), matutils.unitvec(d2)) class Doc2VecVocab(Word2VecVocab): @@ -994,6 +1008,9 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): interval_count = 0 checked_string_types = 0 vocab = defaultdict(int) + max_rawint = -1 # highest raw int tag seen (-1 for none) + doctags_lookup = {} + doctags_list = [] for document_no, document in enumerate(documents): if not checked_string_types: if isinstance(document.words, string_types): @@ -1007,14 +1024,22 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): 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), docvecs.count + document_no, total_words, interval_rate, len(vocab), len(docvecs) ) interval_start = default_timer() interval_count = total_words document_length = len(document.words) for tag in document.tags: - _note_doctag(tag, document_length, docvecs) + # Note a document tag during initial corpus scan, for structure sizing. + if isinstance(tag, integer_types + (integer,)): + max_rawint = max(max_rawint, tag) + else: + if tag in doctags_lookup: + doctags_lookup[tag] = doctags_lookup[tag].repeat(document_length) + else: + doctags_lookup[tag] = Doctag(index=len(doctags_list), word_count=document_length, doc_count=1) + doctags_list.append(tag) for word in document.words: vocab[word] += 1 @@ -1024,7 +1049,18 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule) min_reduce += 1 + if max_rawint > -1: + # adjust indexes/list to account for range of pure-int keyed doctags + for key in doctags_list: + orig = doctags_lookup[key] + doctags_lookup[key] = orig._replace(index=orig.index + max_rawint + 1) + doctags_list = ConcatList([range(0, max_rawint + 1), doctags_list]) + + docvecs.vocab = doctags_lookup + docvecs.index2key = doctags_list corpus_count = document_no + 1 + if len(doctags_list) > corpus_count: + logger.warn("More unique tags (%i) than documents (%i).", len(doctags_list), corpus_count) self.raw_vocab = vocab return total_words, corpus_count @@ -1039,7 +1075,7 @@ def scan_vocab(self, documents=None, corpus_file=None, docvecs=None, progress_pe Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. You may use this argument instead of `documents` to get performance boost. Only one of `documents` or `corpus_file` arguments need to be passed (not both of them). - docvecs : list of :class:`~gensim.models.keyedvectors.Doc2VecKeyedVectors` + docvecs : list of :class:`~gensim.models.keyedvectors.KeyedVectors` The vector representations of the documents in our corpus. Each of them has a size == `vector_size`. progress_per : int Progress will be logged every `progress_per` documents. @@ -1071,52 +1107,11 @@ def scan_vocab(self, documents=None, corpus_file=None, docvecs=None, progress_pe logger.info( "collected %i word types and %i unique tags from a corpus of %i examples and %i words", - len(self.raw_vocab), docvecs.count, corpus_count, total_words + len(self.raw_vocab), len(docvecs), corpus_count, total_words ) return total_words, corpus_count - def indexed_doctags(self, doctag_tokens, docvecs): - """Get the indexes and backing-arrays used in training examples. - - Parameters - ---------- - doctag_tokens : list of {str, int} - A list of tags for which we want the index. - docvecs : list of :class:`~gensim.models.keyedvectors.Doc2VecKeyedVectors` - Vector representations of the documents in the corpus. Each vector has size == `vector_size` - - Returns - ------- - list of int - Indices of the provided tag keys. - - """ - return [ - Doc2VecKeyedVectors._int_index(index, docvecs.doctags, docvecs.max_rawint) - for index in doctag_tokens if self._tag_seen(index, docvecs)] - - def _tag_seen(self, index, docvecs): - """Whether or not the tag exists in our Vocabulary. - - Parameters - ---------- - index : {str, int} - The tag to be checked. - docvecs : :class:`~gensim.models.keyedvectors.Doc2VecKeyedVectors` - Vector representations of the documents in the corpus. Each vector has size == `vector_size` - - Returns - ------- - bool - Whether or not the passed tag exists in our vocabulary. - - """ - if isinstance(index, integer_types + (integer,)): - return index < docvecs.count - else: - return index in docvecs.doctags - class Doc2VecTrainables(Word2VecTrainables): """Represents the inner shallow neural network used to train :class:`~gensim.models.doc2vec.Doc2Vec`.""" @@ -1140,30 +1135,15 @@ def reset_weights(self, hs, negative, wv, docvecs, vocabulary=None): self.reset_doc_weights(docvecs) def reset_doc_weights(self, docvecs): - length = max(len(docvecs.doctags), docvecs.count) + docvecs.resize_vectors() + docvecs.randomly_initialize_vectors() if docvecs.mapfile_path: - docvecs.vectors_docs = np_memmap( - docvecs.mapfile_path + '.vectors_docs', dtype=REAL, mode='w+', shape=(length, docvecs.vector_size) - ) self.vectors_docs_lockf = np_memmap( - docvecs.mapfile_path + '.vectors_docs_lockf', dtype=REAL, mode='w+', shape=(length,) + docvecs.mapfile_path + '.vectors_docs_lockf', dtype=REAL, mode='w+', shape=(len(docvecs.vectors),) ) self.vectors_docs_lockf.fill(1.0) else: - docvecs.vectors_docs = empty((length, docvecs.vector_size), dtype=REAL) - self.vectors_docs_lockf = ones((length,), dtype=REAL) # zeros suppress learning - - for i in range(length): - # construct deterministic seed from index AND model seed - seed = "%d %s" % ( - self.seed, Doc2VecKeyedVectors._index_to_doctag(i, docvecs.offset2doctag, docvecs.max_rawint)) - docvecs.vectors_docs[i] = self.seeded_vector(seed, docvecs.vector_size) - - def get_doctag_trainables(self, doc_words, vector_size): - doctag_vectors = zeros((1, vector_size), dtype=REAL) - doctag_vectors[0] = self.seeded_vector(' '.join(doc_words), vector_size) - doctag_locks = ones(1, dtype=REAL) - return doctag_vectors, doctag_locks + self.vectors_docs_lockf = ones((len(docvecs.vectors),), dtype=REAL) # zeros suppress learning class TaggedBrownCorpus(object): diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 0d500d752c..94935b5956 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -160,6 +160,7 @@ from itertools import chain import logging +from collections import UserList from numbers import Integral try: @@ -167,7 +168,7 @@ except ImportError: from Queue import Queue, Empty # noqa:F401 -from numpy import dot, float32 as REAL, memmap as np_memmap, \ +from numpy import dot, float32 as REAL, \ double, array, zeros, vstack, sqrt, newaxis, integer, \ ndarray, sum as np_sum, prod, argmax, dtype, ascontiguousarray, \ frombuffer @@ -175,7 +176,7 @@ 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, iteritems +from six import string_types, integer_types from six.moves import zip, range from scipy import stats @@ -194,51 +195,85 @@ class KeyedVectors(utils.SaveLoad): """ def __init__(self, vector_size): - self.vectors = zeros((0, vector_size), dtype=REAL) # was syn0, once upon a time - self.vectors_norm = None # was syn0norm, once upon a time - self.vocab = {} + self.vectors = zeros((0, vector_size), dtype=REAL) # fka (formerly known as) syn0 + self.vectors_norm = None # fka syn0norm + self.map = {} self.vector_size = vector_size self.index2key = [] # fka index2entity or index2word - - @classmethod - def load(cls, fname_or_handle, **kwargs): - _kv = super(KeyedVectors, cls).load(fname_or_handle, **kwargs) - # handle rename/consolidation into index2key - return _kv + self.mapfile_path = mapfile_path def _load_specials(self, *args, **kwargs): super(KeyedVectors, self)._load_specials(*args, **kwargs) + if hasattr(self, 'doctags'): + self._upconvert_old_d2vkv() # fixup rename/consolidation into index2key of older index2word, index2entity if not hasattr(self, 'index2key'): self.index2key = self.__dict__.pop('index2word', self.__dict__.pop('index2word', None)) + # fixup rename of vocab into map + if 'map' not in self.__dict__: + self.map = self.__dict__.pop('vocab', None) + + def resize_vectors(self): + """Make vectors match index2key size""" + target_count = len(self.index2key) + prev_count = len(self.vectors) + if prev_count == target_count: + return () + prev_vectors = self.vectors + if hasattr(self, 'mapfile_path') and self.mapfile_path: + self.vectors = np.memmap(self.mapfile_path, shape=(target_count, self.vector_size), mode='w+', dtype=REAL) + else: + self.vectors = np.empty((target_count, self.vector_size), dtype=REAL) + self.vectors[0:min(prev_count, target_count), ] = prev_vectors[0:min(prev_count, target_count), ] + self.vectors_norm = None + return range(prev_count, target_count) + + def randomly_initialize_vectors(self, indexes=None, seed=0): + if indexes is None: + indexes = range(0, len(self.vectors)) + for i in indexes: + self.vectors[i] = pseudorandom_weak_vector(self.vectors.shape[1], + seed_string=(str(self.index2key[i]) + str(seed))) + self.vectors_norm = None - def __getitem__(self, entities): - """Get vector representation of `entities`. + def __len__(self): + return len(self.index2key) + + def __getitem__(self, key_or_keys): + """Get vector representation of `key_or_keys`. Parameters ---------- - entities : {str, list of str} - Input entity/entities. + key_or_keys : {str, list of str, int, list of int} + Requested key or list-of-keys Returns ------- numpy.ndarray - Vector representation for `entities` (1D if `entities` is string, otherwise - 2D). + Vector representation for `key_or_keys` (1D if `key_or_keys` is single key, 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) + if isinstance(key_or_keys, (string_types, integer_types, np.integer)): + return self.get_vector(key_or_keys) - return vstack([self.get_vector(entity) for entity in entities]) + return vstack([self.get_vector(key) for key in key_or_keys]) + + def get_index(self, key): + """TODO comment""" + if key in self.map: + return self.map[key].index + elif isinstance(key, (integer_types, np.integer)) and key < len(self.vectors): + return key + else: + raise KeyError("Key '%s' not in vocabulary" % key) def get_vector(self, key, use_norm=False): """Get the entity's representations in vector space, as a 1D numpy array. Parameters ---------- - key : str - Identifier of the vector to return + key : str or int + Key for vector to return, or int slot use_norm : bool, optional If True - resulting vector will be L2-normalized (unit euclidean length). @@ -253,15 +288,14 @@ def get_vector(self, key, use_norm=False): If the given key doesn't exist. """ - if key in self.vocab: - if use_norm: - result = self.vectors_norm[self.vocab[key].index] - else: - result = self.vectors[self.vocab[key].index] - result.setflags(write=False) - return result + index = self.get_index(key) + if use_norm: + result = self.vectors_norm[index] else: - raise KeyError("Key '%s' not in vocabulary" % key) + result = self.vectors[index] + + result.setflags(write=False) + return result def word_vec(self, *args, **kwargs): """Compatibility alias for get_vector()""" @@ -326,8 +360,15 @@ def __setitem__(self, entities, weights): self.add(entities, weights, replace=True) + def has_index_for(self, key): + """Can this model return an index for this key?""" + try: + return self.get_index(key) >= 0 + except KeyError: + return False + def __contains__(self, key): - return key in self.vocab + return self.has_index_for(key) def most_similar_to_given(self, entity1, entities_list): """Get the `entity` from `entities_list` most similar to `entity1`.""" @@ -365,6 +406,14 @@ def index2word(self): def index2word(self, value): self.index2key = value + @property + def vocab(self): + return self.map + + @vocab.setter + def vocab(self, value): + self.map = value + def save(self, *args, **kwargs): """Save KeyedVectors. @@ -383,7 +432,8 @@ def save(self, *args, **kwargs): kwargs['ignore'] = kwargs.get('ignore', ['vectors_norm']) super(KeyedVectors, self).save(*args, **kwargs) - def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=None, indexer=None): + def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip_end=None, + restrict_vocab=None, indexer=None): """Find the top-N most similar words. Positive words contribute positively towards the similarity, negative words negatively. @@ -401,11 +451,16 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non topn : int or None, optional Number of top-N similar words to return, when `topn` is int. When `topn` is None, then similarities for all words are returned. + clip_start : int + Start clipping index. + clip_end : int + End clipping index. 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.) + meaningful if you've sorted the vocabulary by descending frequency.) If + specified, overrides any values of clip_start or clip_end Returns ------- @@ -424,19 +479,24 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non negative = [] self.init_sims() + clip_end = clip_end or len(self.vectors_norm) - if isinstance(positive, string_types) and not negative: + if restrict_vocab: + clip_start = 0 + clip_end = restrict_vocab + + 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 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 + (key, 1.0) if isinstance(key, string_types + integer_types + (ndarray, integer)) + else key for key in positive ] negative = [ - (word, -1.0) if isinstance(word, string_types + (ndarray,)) else word - for word in negative + (key, -1.0) if isinstance(key, string_types + integer_types + (ndarray, integer)) + else key for key in negative ] # compute the weighted average of all words @@ -446,8 +506,8 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non 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 self.has_index_for(word): + all_words.add(self.get_index(word)) if not mean: raise ValueError("cannot compute similarity with no input") mean = matutils.unitvec(array(mean).mean(axis=0)).astype(REAL) @@ -455,13 +515,13 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non if indexer is not None and isinstance(topn, int): 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) + dists = dot(self.vectors_norm[clip_start:clip_end], 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.index2key[sim], float(dists[sim])) for sim in best if sim not in all_words] + result = [(self.index2key[sim + clip_start], float(dists[sim])) + for sim in best if (sim + clip_start) not in all_words] return result[:topn] def similar_by_word(self, word, topn=10, restrict_vocab=None): @@ -1136,25 +1196,57 @@ def relative_cosine_similarity(self, wa, wb, topn=10): return rcs - def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None): + def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, write_first_line=True, + prefix='', append=False): """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 + The file path used to save the vectors in. fvocab : str, optional - Optional file path used to save the vocabulary + File path used to save the vocabulary. binary : bool, optional - If True, the data will be saved in binary word2vec format, else it will be saved in plain text. + 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 + Explicitly specify total number of vectors (in case word vectors are appended with document vectors afterwards). - + TODO: doc other params """ - _save_word2vec_format( - fname, self.vocab, self.vectors, fvocab=fvocab, binary=binary, total_vec=total_vec) + if total_vec is None: + total_vec = len(self.index2key) + mode = 'wb' if not append else 'ab' + sorted_vocab_keys = sorted(self.vocab.keys(), key=lambda k: -self.vocab[k].count) + + if fvocab is not None: + logger.info("storing vocabulary in %s", fvocab) + with utils.open(fvocab, mode) as vout: + for word in sorted_vocab_keys: + vout.write(utils.to_utf8("%s%s %s\n" % (prefix, word, self.vocab[word].count))) + + logger.info("storing %sx%s projection weights into %s", total_vec, self.vector_size, fname) + assert (len(self.index2key), self.vector_size) == self.vectors.shape + + # after (possibly-empty) initial range of int-only keys, + # store in sorted order: most frequent keys at the top + index_id_count = 0 + for i, val in enumerate(self.index2key): + if not (i == val): + break + index_id_count += 1 + keys_to_write = chain(range(0, index_id_count), sorted_vocab_keys) + + with utils.open(fname, mode) as fout: + if write_first_line: + fout.write(utils.to_utf8("%s %s\n" % (total_vec, self.vector_size))) + for key in keys_to_write: + row = self[key] + if binary: + row = row.astype(REAL) + fout.write(utils.to_utf8(prefix + str(key)) + b" " + row.tostring()) + else: + fout.write(utils.to_utf8("%s%s %s\n" % (prefix, str(key), ' '.join(repr(val) for val in row)))) @classmethod def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', @@ -1238,391 +1330,28 @@ def get_keras_embedding(self, train_embeddings=False): ) return layer + def _upconvert_old_d2vkv(self): + from gensim.models.doc2vec import Doctag + self.vocab = self.doctags + for k in self.vocab.keys(): + v = self.vocab[k] + if hasattr(v, 'offset'): + self.vocab[k] = Doctag(v.offset + self.max_rawint + 1, v.word_count, v.doc_count) + if(self.max_rawint > -1): + self.index2key = ConcatList([range(0, self.max_rawint + 1), self.offset2doctag]) + self.vectors = self.vectors_docs + del self.doctags + del self.vectors_docs + del self.count + del self.max_rawint + + def similarity_unseen_docs(self, *args, **kwargs): + raise NotImplementedError("Call similarity_unseen_docs on a Doc2Vec model instead.") + # to help 3.8.1 & older pickles load properly Word2VecKeyedVectors = KeyedVectors - - -class Doc2VecKeyedVectors(KeyedVectors): - - 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 - - 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 not replace and 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 = _l2_norm(self.vectors_docs, replace=replace) - - 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 or None, optional - Number of top-N similar docvecs to return, when `topn` is int. When `topn` is None, - then similarities for all docvecs are returned. - clip_start : int - Start clipping index. - clip_end : int - End clipping index. - - Returns - ------- - list of ({str, int}, float) - Sequence of (doctag/index, similarity). - - """ - if isinstance(topn, Integral) and topn < 1: - return [] - - 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 and isinstance(topn, int): - 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 - KeyedVectors.cosine_similarities(input_vector, other_vectors) - - def similarity_unseen_docs(self, model, doc_words1, doc_words2, alpha=None, min_alpha=None, steps=None): - """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 will 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.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 +Doc2VecKeyedVectors = KeyedVectors def _l2_norm(m, replace=False): @@ -1665,54 +1394,8 @@ def __str__(self): return "%s(%s)" % (self.__class__.__name__, ', '.join(vals)) -def _save_word2vec_format(fname, vocab, vectors, 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. - vocab : dict - The vocabulary of words. - vectors : numpy.array - The vectors to be stored. - fvocab : str, 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 - Explicitly specify total number of vectors - (in case word vectors are appended with document vectors afterwards). - - """ - if not (vocab or vectors): - raise RuntimeError("no input") - if total_vec is None: - total_vec = len(vocab) - vector_size = vectors.shape[1] - if fvocab is not None: - logger.info("storing vocabulary in %s", fvocab) - with utils.open(fvocab, 'wb') as vout: - for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count): - vout.write(utils.to_utf8("%s %s\n" % (word, vocab_.count))) - logger.info("storing %sx%s projection weights into %s", total_vec, vector_size, fname) - assert (len(vocab), vector_size) == vectors.shape - with utils.open(fname, 'wb') as fout: - fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size))) - # store in sorted order: most frequent words at the top - for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count): - row = vectors[vocab_.index] - if binary: - row = row.astype(REAL) - fout.write(utils.to_utf8(word) + b" " + row.tostring()) - else: - fout.write(utils.to_utf8("%s %s\n" % (word, ' '.join(repr(val) for val in row)))) - - # Functions for internal use by _load_word2vec_format function - def _add_word_to_result(result, counts, word, weights, vocab_size): word_id = len(result.vocab) @@ -1859,3 +1542,41 @@ def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8' logger.info("loaded %s matrix from %s", result.vectors.shape, fname) return result + + +def load_word2vec_format(*args, **kwargs): + return KeyedVectors.load_word2vec_format(*args, **kwargs) + + +def pseudorandom_weak_vector(size, seed_string=None, hashfxn=hash): + """Get a 'random' vector (but somewhat deterministic, at least + within the same Python 3 launch or PYTHONHASHSEED, if seed_string + supplied). + + Useful for initializing KeyedVectors that will be the starting + projection/input layers of *2Vec models. + """ + if seed_string: + once = np.random.RandomState(hashfxn(seed_string) & 0xffffffff) + else: + once = np.random + return (once.rand(size).astype(REAL) - 0.5) / size + + +class ConcatList(UserList): + def __getitem__(self, index): + for subseq in self.data: + if index >= len(subseq): + index -= len(subseq) + continue + return subseq[index] + else: + raise IndexError("ConcatList index out of range") + + def __iter__(self): + return iter(chain(*self.data)) + + def __len__(self): + return sum(len(subseq) for subseq in self.data) + +# TODO: implement or stub as NotImplemented other methods diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 20204cdc86..0c36fd9d67 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -131,8 +131,8 @@ import itertools import warnings -from gensim.utils import keep_vocab_item, call_on_class_only -from gensim.models.keyedvectors import Vocab, KeyedVectors +from gensim.utils import keep_vocab_item, call_on_class_only, deprecated +from gensim.models.keyedvectors import Vocab, KeyedVectors, pseudorandom_weak_vector from gensim.models.base_any2vec import BaseWordEmbeddingsModel try: @@ -140,9 +140,9 @@ except ImportError: from Queue import Queue, Empty -from numpy import exp, dot, zeros, random, dtype, float32 as REAL,\ +from numpy import exp, dot, zeros, dtype, float32 as REAL,\ uint32, seterr, array, uint8, vstack, fromstring, sqrt,\ - empty, sum as np_sum, ones, logaddexp, log, outer + sum as np_sum, ones, logaddexp, log, outer from scipy.special import expit @@ -1599,38 +1599,28 @@ def prepare_weights(self, hs, negative, wv, update=False, vocabulary=None): else: self.update_weights(hs, negative, wv) + @deprecated("Use gensim.models.keyedvectors.pseudorandom_weak_vector() directly") def seeded_vector(self, seed_string, vector_size): - """Get a random vector (but deterministic by seed_string).""" - # Note: built-in hash() may vary by Python version or even (in Py3.x) per launch - once = random.RandomState(self.hashfxn(seed_string) & 0xffffffff) - return (once.rand(vector_size) - 0.5) / vector_size + return pseudorandom_weak_vector(vector_size, seed_string=seed_string, hashfxn=self.hashfxn) def reset_weights(self, hs, negative, wv): """Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary.""" logger.info("resetting layer weights") - wv.vectors = empty((len(wv.vocab), wv.vector_size), dtype=REAL) - # randomize weights vector by vector, rather than materializing a huge random matrix in RAM at once - for i in range(len(wv.vocab)): - # construct deterministic seed from word AND seed argument - wv.vectors[i] = self.seeded_vector(wv.index2key[i] + str(self.seed), wv.vector_size) + wv.resize_vectors() + wv.randomly_initialize_vectors(seed=self.seed) if hs: self.syn1 = zeros((len(wv.vocab), self.layer1_size), dtype=REAL) if negative: self.syn1neg = zeros((len(wv.vocab), self.layer1_size), dtype=REAL) - wv.vectors_norm = None self.vectors_lockf = ones(len(wv.vocab), dtype=REAL) # zeros suppress learning def update_weights(self, hs, negative, wv): """Copy all the existing weights, and reset the weights for the newly added vocabulary.""" logger.info("updating layer weights") - gained_vocab = len(wv.vocab) - len(wv.vectors) - newvectors = empty((gained_vocab, wv.vector_size), dtype=REAL) - - # randomize the remaining words - for i in range(len(wv.vectors), len(wv.vocab)): - # construct deterministic seed from word AND seed argument - newvectors[i - len(wv.vectors)] = self.seeded_vector(wv.index2key[i] + str(self.seed), wv.vector_size) + new_range = wv.resize_vectors() + gained_vocab = len(new_range) + wv.randomly_initialize_vectors(indexes=new_range) # Raise an error if an online update is run before initial training on a corpus if not len(wv.vectors): @@ -1639,8 +1629,6 @@ def update_weights(self, hs, negative, wv): "First build the vocabulary of your model with a corpus before doing an online update." ) - wv.vectors = vstack([wv.vectors, newvectors]) - if hs: self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=REAL)]) if negative: diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index 70e071cf51..f255d612e6 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -88,8 +88,8 @@ def testPersistenceWord2VecFormat(self): model = doc2vec.Doc2Vec(DocsLeeCorpus(), min_count=1) # test saving both document and word embedding test_doc_word = get_tmpfile('gensim_doc2vec.dw') - model.save_word2vec_format(test_doc_word, doctag_vec=True, word_vec=True, binary=True) - binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_doc_word, binary=True) + model.save_word2vec_format(test_doc_word, doctag_vec=True, word_vec=True, binary=False) + binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_doc_word, binary=False) self.assertEqual(len(model.wv.vocab) + len(model.docvecs), len(binary_model_dv.vocab)) # test saving document embedding only test_doc = get_tmpfile('gensim_doc2vec.d') @@ -115,10 +115,9 @@ def testLoadOldModel(self): self.assertTrue(model.trainables.vectors_lockf.shape == (3955, )) self.assertTrue(model.vocabulary.cum_table.shape == (3955, )) - self.assertTrue(model.docvecs.vectors_docs.shape == (300, 100)) + self.assertTrue(model.docvecs.vectors.shape == (300, 100)) self.assertTrue(model.trainables.vectors_docs_lockf.shape == (300, )) - self.assertTrue(model.docvecs.max_rawint == 299) - self.assertTrue(model.docvecs.count == 300) + self.assertTrue(len(model.docvecs) == 300) self.model_sanity(model) @@ -132,11 +131,9 @@ def testLoadOldModel(self): self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) self.assertTrue(model.trainables.vectors_lockf.shape == (3955, )) self.assertTrue(model.vocabulary.cum_table.shape == (3955, )) - - self.assertTrue(model.docvecs.vectors_docs.shape == (300, 100)) + self.assertTrue(model.docvecs.vectors.shape == (300, 100)) self.assertTrue(model.trainables.vectors_docs_lockf.shape == (300, )) - self.assertTrue(model.docvecs.max_rawint == 299) - self.assertTrue(model.docvecs.count == 300) + self.assertTrue(len(model.docvecs) == 300) self.model_sanity(model) @@ -159,8 +156,8 @@ def testLoadOldModel(self): self.assertTrue(len(model.wv.vocab) == 3) self.assertIsNone(model.corpus_total_words) self.assertTrue(model.wv.vectors.shape == (3, 4)) - self.assertTrue(model.docvecs.vectors_docs.shape == (2, 4)) - self.assertTrue(model.docvecs.count == 2) + self.assertTrue(model.docvecs.vectors.shape == (2, 4)) + self.assertTrue(len(model.docvecs) == 2) # check if inferring vectors for new documents and similarity search works. doc0_inferred = model.infer_vector(list(DocsLeeCorpus())[0].words) sims_to_infer = model.docvecs.most_similar([doc0_inferred], topn=len(model.docvecs)) @@ -297,7 +294,7 @@ def test_int_doctags(self): model = doc2vec.Doc2Vec(min_count=1) model.build_vocab(corpus) - self.assertEqual(len(model.docvecs.vectors_docs), 300) + self.assertEqual(len(model.docvecs.vectors), 300) self.assertEqual(model.docvecs[0].shape, (100,)) self.assertEqual(model.docvecs[np.int64(0)].shape, (100,)) self.assertRaises(KeyError, model.__getitem__, '_*0') @@ -321,19 +318,17 @@ def test_string_doctags(self): model = doc2vec.Doc2Vec(min_count=1) model.build_vocab(corpus) - self.assertEqual(len(model.docvecs.vectors_docs), 300) + self.assertEqual(len(model.docvecs.vectors), 300) self.assertEqual(model.docvecs[0].shape, (100,)) self.assertEqual(model.docvecs['_*0'].shape, (100,)) self.assertTrue(all(model.docvecs['_*0'] == model.docvecs[0])) - self.assertTrue(max(d.offset for d in model.docvecs.doctags.values()) < len(model.docvecs.doctags)) - self.assertTrue( - max( - model.docvecs._int_index(str_key, model.docvecs.doctags, model.docvecs.max_rawint) - for str_key in model.docvecs.doctags.keys()) - < len(model.docvecs.vectors_docs) + self.assertTrue(max(d.index for d in model.docvecs.map.values()) < len(model.docvecs.index2key)) + self.assertLess( + max(model.docvecs.get_index(str_key) for str_key in model.docvecs.map.keys()), + len(model.docvecs.vectors) ) # verify docvecs.most_similar() returns string doctags rather than indexes - self.assertEqual(model.docvecs.offset2doctag[0], model.docvecs.most_similar([model.docvecs[0]])[0][0]) + self.assertEqual(model.docvecs.index2key[0], model.docvecs.most_similar([model.docvecs[0]])[0][0]) def test_empty_errors(self): # no input => "RuntimeError: you must first build vocabulary before training the model" @@ -344,15 +339,15 @@ def test_empty_errors(self): def test_similarity_unseen_docs(self): """Test similarity of out of training sentences""" - rome_str = ['rome', 'italy'] - car_str = ['car'] + rome_words = ['rome', 'italy'] + car_words = ['car'] corpus = list(DocsLeeCorpus(True)) model = doc2vec.Doc2Vec(min_count=1) model.build_vocab(corpus) self.assertTrue( - model.docvecs.similarity_unseen_docs(model, rome_str, rome_str) - > model.docvecs.similarity_unseen_docs(model, rome_str, car_str) + model.similarity_unseen_docs(rome_words, rome_words) + > model.similarity_unseen_docs(rome_words, car_words) ) def model_sanity(self, model, keep_training=True): @@ -405,7 +400,7 @@ def test_training(self): corpus = DocsLeeCorpus() model = doc2vec.Doc2Vec(vector_size=100, min_count=2, epochs=20, workers=1) model.build_vocab(corpus) - self.assertEqual(model.docvecs.vectors_docs.shape, (300, 100)) + self.assertEqual(model.docvecs.vectors.shape, (300, 100)) model.train(corpus, total_examples=model.corpus_count, epochs=model.epochs) self.model_sanity(model) @@ -422,7 +417,7 @@ def test_training_fromfile(self): model = doc2vec.Doc2Vec(vector_size=100, min_count=2, epochs=20, workers=1) model.build_vocab(corpus_file=corpus_file) - self.assertEqual(model.docvecs.vectors_docs.shape, (300, 100)) + self.assertEqual(model.docvecs.vectors.shape, (300, 100)) model.train(corpus_file=corpus_file, total_words=model.corpus_total_words, epochs=model.epochs) self.model_sanity(model) @@ -606,12 +601,12 @@ def test_deterministic_dmc(self): self.models_equal(model, model2) def test_mixed_tag_types(self): - """Ensure alternating int/string tags don't share indexes in vectors_docs""" + """Ensure alternating int/string tags don't share indexes in vectors""" mixed_tag_corpus = [doc2vec.TaggedDocument(words, [i, words[0]]) for i, words in enumerate(raw_sentences)] model = doc2vec.Doc2Vec() model.build_vocab(mixed_tag_corpus) - expected_length = len(sentences) + len(model.docvecs.doctags) # 9 sentences, 7 unique first tokens - self.assertEqual(len(model.docvecs.vectors_docs), expected_length) + expected_length = len(sentences) + len(model.docvecs.map) # 9 sentences, 7 unique first tokens + self.assertEqual(len(model.docvecs.vectors), expected_length) def models_equal(self, model, model2): # check words/hidden-weights @@ -622,8 +617,8 @@ def models_equal(self, model, model2): if model.negative: self.assertTrue(np.allclose(model.trainables.syn1neg, model2.trainables.syn1neg)) # check docvecs - self.assertEqual(len(model.docvecs.doctags), len(model2.docvecs.doctags)) - self.assertEqual(len(model.docvecs.offset2doctag), len(model2.docvecs.offset2doctag)) + self.assertEqual(len(model.docvecs.map), len(model2.docvecs.map)) + self.assertEqual(len(model.docvecs.index2key), len(model2.docvecs.index2key)) def test_word_vec_non_writeable(self): model = keyedvectors.KeyedVectors.load_word2vec_format(datapath('word2vec_pre_kv_c')) diff --git a/gensim/test/test_translation_matrix.py b/gensim/test/test_translation_matrix.py index 7be7ce4b63..f6798ac9cc 100644 --- a/gensim/test/test_translation_matrix.py +++ b/gensim/test/test_translation_matrix.py @@ -3,6 +3,7 @@ from collections import namedtuple import unittest import math +import logging import numpy as np @@ -116,3 +117,8 @@ def test_infer_vector(self): eps = 1e-6 caculated = cosine(self.target_doc_vec.docvecs[self.train_docs[5].tags], infered_vec) self.assertLessEqual(math.fabs(caculated - expected), eps) + + +if __name__ == '__main__': + logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) + unittest.main() From d4267f81158cad0915ce546f78a05c814dd3c4d7 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 12 Dec 2019 14:40:29 -0800 Subject: [PATCH 07/60] fix docstring warnings; update usages --- gensim/models/keyedvectors.py | 2 +- gensim/similarities/nmslib.py | 4 ++-- gensim/test/test_similarities.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 94935b5956..839ae463dc 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -1554,7 +1554,7 @@ def pseudorandom_weak_vector(size, seed_string=None, hashfxn=hash): supplied). Useful for initializing KeyedVectors that will be the starting - projection/input layers of *2Vec models. + projection/input layers of _2Vec models. """ if seed_string: once = np.random.RandomState(hashfxn(seed_string) & 0xffffffff) diff --git a/gensim/similarities/nmslib.py b/gensim/similarities/nmslib.py index 77fa1fdd74..b7ed1f0df4 100644 --- a/gensim/similarities/nmslib.py +++ b/gensim/similarities/nmslib.py @@ -189,8 +189,8 @@ def _build_from_doc2vec(self): docvecs = self.model.docvecs docvecs.init_sims() - labels = [docvecs.index_to_doctag(i) for i in range(0, docvecs.count)] - self._build_from_model(docvecs.vectors_docs_norm, labels) + labels = [docvecs.index2key[i] for i in range(0, len(docvecs))] + self._build_from_model(docvecs.vectors_norm, labels) def _build_from_keyedvectors(self): """Build an NMSLIB index using word vectors from a KeyedVectors model.""" diff --git a/gensim/test/test_similarities.py b/gensim/test/test_similarities.py index 4d049350e5..1b4c0174a4 100644 --- a/gensim/test/test_similarities.py +++ b/gensim/test/test_similarities.py @@ -657,7 +657,7 @@ def setUp(self): self.model = doc2vec.Doc2Vec(sentences, min_count=1) self.model.docvecs.init_sims() self.index = AnnoyIndexer(self.model, 300) - self.vector = self.model.docvecs.vectors_docs_norm[0] + self.vector = self.model.docvecs.vectors_norm[0] def testDocumentIsSimilarToItself(self): approx_neighbors = self.index.most_similar(self.vector, 1) @@ -809,7 +809,7 @@ def setUp(self): self.model = doc2vec.Doc2Vec(sentences, min_count=1) self.model.docvecs.init_sims() self.index = NmslibIndexer(self.model) - self.vector = self.model.docvecs.vectors_docs_norm[0] + self.vector = self.model.docvecs.vectors_norm[0] def test_document_is_similar_to_itself(self): approx_neighbors = self.index.most_similar(self.vector, 1) From f6e7aa6f25d2ca065021a5f5f2f9117fea59fd6a Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 12 Dec 2019 17:03:14 -0800 Subject: [PATCH 08/60] rm unused old plain-python codepaths --- gensim/models/doc2vec.py | 1 - gensim/models/word2vec.py | 215 +------------------------------------- 2 files changed, 1 insertion(+), 215 deletions(-) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 5045d08aa4..537a1e7f94 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -80,7 +80,6 @@ from gensim.utils import call_on_class_only from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc from gensim.models.word2vec import Word2VecVocab, Word2VecTrainables -from gensim.models.word2vec import train_cbow_pair, train_sg_pair, train_batch_sg # noqa from six.moves import range from six import string_types, integer_types, itervalues from gensim.models.base_any2vec import BaseWordEmbeddingsModel diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 0c36fd9d67..9129c84cac 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -142,9 +142,7 @@ from numpy import exp, dot, zeros, dtype, float32 as REAL,\ uint32, seterr, array, uint8, vstack, fromstring, sqrt,\ - sum as np_sum, ones, logaddexp, log, outer - -from scipy.special import expit + sum as np_sum, ones, logaddexp from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc from six import iteritems, itervalues, string_types @@ -179,217 +177,6 @@ def train_epoch_cbow(model, corpus_file, offset, _cython_vocab, _cur_epoch, _exp raise RuntimeError("Training with corpus_file argument is not supported") -def train_sg_pair(model, word, context_index, alpha, learn_vectors=True, learn_hidden=True, - context_vectors=None, context_locks=None, compute_loss=False, is_ft=False): - """Train the passed model instance on a word and its context, using the Skip-gram algorithm. - - Parameters - ---------- - model : :class:`~gensim.models.word2vec.Word2Vec` - The model to be trained. - word : str - The label (predicted) word. - context_index : list of int - The vocabulary indices of the words in the context. - alpha : float - Learning rate. - learn_vectors : bool, optional - Whether the vectors should be updated. - learn_hidden : bool, optional - Whether the weights of the hidden layer should be updated. - context_vectors : list of list of float, optional - Vector representations of the words in the context. If None, these will be retrieved from the model. - context_locks : list of float, optional - The lock factors for each word in the context. - compute_loss : bool, optional - Whether or not the training loss should be computed. - is_ft : bool, optional - If True, weights will be computed using `model.wv.syn0_vocab` and `model.wv.syn0_ngrams` - instead of `model.wv.syn0`. - - Returns - ------- - numpy.ndarray - Error vector to be back-propagated. - - """ - if context_vectors is None: - if is_ft: - context_vectors_vocab = model.wv.syn0_vocab - context_vectors_ngrams = model.wv.syn0_ngrams - else: - context_vectors = model.wv.syn0 - if context_locks is None: - if is_ft: - context_locks_vocab = model.syn0_vocab_lockf - context_locks_ngrams = model.syn0_ngrams_lockf - else: - context_locks = model.syn0_lockf - - if word not in model.wv.vocab: - return - predict_word = model.wv.vocab[word] # target word (NN output) - - if is_ft: - l1_vocab = context_vectors_vocab[context_index[0]] - l1_ngrams = np_sum(context_vectors_ngrams[context_index[1:]], axis=0) - if context_index: - l1 = np_sum([l1_vocab, l1_ngrams], axis=0) / len(context_index) - else: - l1 = context_vectors[context_index] # input word (NN input/projection layer) - lock_factor = context_locks[context_index] - - neu1e = zeros(l1.shape) - - if model.hs: - # work on the entire tree at once, to push as much work into numpy's C routines as possible (performance) - l2a = deepcopy(model.syn1[predict_word.point]) # 2d matrix, codelen x layer1_size - prod_term = dot(l1, l2a.T) - fa = expit(prod_term) # propagate hidden -> output - ga = (1 - predict_word.code - fa) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1[predict_word.point] += outer(ga, l1) # learn hidden -> output - neu1e += dot(ga, l2a) # save error - - # loss component corresponding to hierarchical softmax - if compute_loss: - sgn = (-1.0) ** predict_word.code # `ch` function, 0 -> 1, 1 -> -1 - lprob = -log(expit(-sgn * prod_term)) - model.running_training_loss += sum(lprob) - - if model.negative: - # use this word (label = 1) + `negative` other random words not from this sentence (label = 0) - word_indices = [predict_word.index] - while len(word_indices) < model.negative + 1: - w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1])) - if w != predict_word.index: - word_indices.append(w) - l2b = model.syn1neg[word_indices] # 2d matrix, k+1 x layer1_size - prod_term = dot(l1, l2b.T) - fb = expit(prod_term) # propagate hidden -> output - gb = (model.neg_labels - fb) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1neg[word_indices] += outer(gb, l1) # learn hidden -> output - neu1e += dot(gb, l2b) # save error - - # loss component corresponding to negative sampling - if compute_loss: - model.running_training_loss -= sum(log(expit(-1 * prod_term[1:]))) # for the sampled words - model.running_training_loss -= log(expit(prod_term[0])) # for the output word - - if learn_vectors: - if is_ft: - model.wv.syn0_vocab[context_index[0]] += neu1e * context_locks_vocab[context_index[0]] - for i in context_index[1:]: - model.wv.syn0_ngrams[i] += neu1e * context_locks_ngrams[i] - else: - l1 += neu1e * lock_factor # learn input -> hidden (mutates model.wv.syn0[word2.index], if that is l1) - return neu1e - - -def train_cbow_pair(model, word, input_word_indices, l1, alpha, learn_vectors=True, learn_hidden=True, - compute_loss=False, context_vectors=None, context_locks=None, is_ft=False): - """Train the passed model instance on a word and its context, using the CBOW algorithm. - - Parameters - ---------- - model : :class:`~gensim.models.word2vec.Word2Vec` - The model to be trained. - word : str - The label (predicted) word. - input_word_indices : list of int - The vocabulary indices of the words in the context. - l1 : list of float - Vector representation of the label word. - alpha : float - Learning rate. - learn_vectors : bool, optional - Whether the vectors should be updated. - learn_hidden : bool, optional - Whether the weights of the hidden layer should be updated. - compute_loss : bool, optional - Whether or not the training loss should be computed. - context_vectors : list of list of float, optional - Vector representations of the words in the context. If None, these will be retrieved from the model. - context_locks : list of float, optional - The lock factors for each word in the context. - is_ft : bool, optional - If True, weights will be computed using `model.wv.syn0_vocab` and `model.wv.syn0_ngrams` - instead of `model.wv.syn0`. - - Returns - ------- - numpy.ndarray - Error vector to be back-propagated. - - """ - if context_vectors is None: - if is_ft: - context_vectors_vocab = model.wv.syn0_vocab - context_vectors_ngrams = model.wv.syn0_ngrams - else: - context_vectors = model.wv.syn0 - if context_locks is None: - if is_ft: - context_locks_vocab = model.syn0_vocab_lockf - context_locks_ngrams = model.syn0_ngrams_lockf - else: - context_locks = model.syn0_lockf - - neu1e = zeros(l1.shape) - - if model.hs: - l2a = model.syn1[word.point] # 2d matrix, codelen x layer1_size - prod_term = dot(l1, l2a.T) - fa = expit(prod_term) # propagate hidden -> output - ga = (1. - word.code - fa) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1[word.point] += outer(ga, l1) # learn hidden -> output - neu1e += dot(ga, l2a) # save error - - # loss component corresponding to hierarchical softmax - if compute_loss: - sgn = (-1.0) ** word.code # ch function, 0-> 1, 1 -> -1 - model.running_training_loss += sum(-log(expit(-sgn * prod_term))) - - if model.negative: - # use this word (label = 1) + `negative` other random words not from this sentence (label = 0) - word_indices = [word.index] - while len(word_indices) < model.negative + 1: - w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1])) - if w != word.index: - word_indices.append(w) - l2b = model.syn1neg[word_indices] # 2d matrix, k+1 x layer1_size - prod_term = dot(l1, l2b.T) - fb = expit(prod_term) # propagate hidden -> output - gb = (model.neg_labels - fb) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1neg[word_indices] += outer(gb, l1) # learn hidden -> output - neu1e += dot(gb, l2b) # save error - - # loss component corresponding to negative sampling - if compute_loss: - model.running_training_loss -= sum(log(expit(-1 * prod_term[1:]))) # for the sampled words - model.running_training_loss -= log(expit(prod_term[0])) # for the output word - - if learn_vectors: - # learn input -> hidden, here for all words in the window separately - if is_ft: - if not model.cbow_mean and input_word_indices: - neu1e /= (len(input_word_indices[0]) + len(input_word_indices[1])) - for i in input_word_indices[0]: - context_vectors_vocab[i] += neu1e * context_locks_vocab[i] - for i in input_word_indices[1]: - context_vectors_ngrams[i] += neu1e * context_locks_ngrams[i] - else: - if not model.cbow_mean and input_word_indices: - neu1e /= len(input_word_indices) - for i in input_word_indices: - context_vectors[i] += neu1e * context_locks[i] - - return neu1e - - def score_sg_pair(model, word, word2): """Score the trained Skip-gram model on a pair of words. From 470b1198f4fd80a940b8148a61e6c52c4c0dd020 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Fri, 13 Dec 2019 16:39:21 -0800 Subject: [PATCH 09/60] unify class comments under __init__ for consistncy w/ api doc presentation --- gensim/models/base_any2vec.py | 82 ++++++++--------- gensim/models/doc2vec.py | 92 +++++++++---------- gensim/models/fasttext.py | 160 +++++++++++++++++----------------- gensim/models/keyedvectors.py | 20 ++--- gensim/models/word2vec.py | 109 +++++++++++------------ 5 files changed, 220 insertions(+), 243 deletions(-) diff --git a/gensim/models/base_any2vec.py b/gensim/models/base_any2vec.py index f6d4f77090..f0a33ba7ff 100644 --- a/gensim/models/base_any2vec.py +++ b/gensim/models/base_any2vec.py @@ -54,27 +54,16 @@ class BaseAny2VecModel(utils.SaveLoad): - r"""Base class for training, using and evaluating \*2vec model. - - Contains implementation for multi-threaded training. The purpose of this class is to provide a - reference interface for concrete embedding implementations, whether the input space is a corpus - of words, documents or anything else. At the same time, functionality that we expect to be common - for those implementations is provided here to avoid code duplication. - - In the special but usual case where the input space consists of words, a more specialized layer - is provided, consider inheriting from :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` - - Notes - ----- - A subclass should initialize the following attributes: + def __init__(self, workers=3, vector_size=100, epochs=5, callbacks=(), batch_words=10000): + r"""Base class for training, using and evaluating \*2vec model. - * self.kv - keyed vectors in model (see :class:`~gensim.models.keyedvectors.Word2VecKeyedVectors` as example) - * self.vocabulary - vocabulary (see :class:`~gensim.models.word2vec.Word2VecVocab` as example) - * self.trainables - internal matrices (see :class:`~gensim.models.word2vec.Word2VecTrainables` as example) + Contains implementation for multi-threaded training. The purpose of this class is to provide a + reference interface for concrete embedding implementations, whether the input space is a corpus + of words, documents or anything else. At the same time, functionality that we expect to be common + for those implementations is provided here to avoid code duplication. - """ - def __init__(self, workers=3, vector_size=100, epochs=5, callbacks=(), batch_words=10000): - """ + In the special but usual case where the input space consists of words, a more specialized layer + is provided, consider inheriting from :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` Parameters ---------- @@ -89,6 +78,14 @@ def __init__(self, workers=3, vector_size=100, epochs=5, callbacks=(), batch_wor batch_words : int, optional Number of words to be processed by a single job. + Notes + ----- + A subclass should initialize the following attributes: + + * self.kv - keyed vectors in model (see :class:`~gensim.models.keyedvectors.Word2VecKeyedVectors` as example) + * self.vocabulary - vocabulary (see :class:`~gensim.models.word2vec.Word2VecVocab` as example) + * self.trainables - internal matrices (see :class:`~gensim.models.word2vec.Word2VecTrainables` as example) + """ self.vector_size = int(vector_size) self.workers = int(workers) @@ -601,7 +598,7 @@ def load(cls, fname_or_handle, **kwargs): return super(BaseAny2VecModel, cls).load(fname_or_handle, **kwargs) def save(self, fname_or_handle, **kwargs): - """"Save the object to file. + """Save the object to file. Parameters ---------- @@ -620,33 +617,10 @@ def save(self, fname_or_handle, **kwargs): class BaseWordEmbeddingsModel(BaseAny2VecModel): - """Base class containing common methods for training, using & evaluating word embeddings learning models. - - See Also - -------- - :class:`~gensim.models.word2vec.Word2Vec`. - Word2Vec model - embeddings for words. - :class:`~gensim.models.fasttext.FastText`. - FastText model - embeddings for words (ngram-based). - :class:`~gensim.models.doc2vec.Doc2Vec`. - Doc2Vec model - embeddings for documents. - :class:`~gensim.models.poincare.PoincareModel` - Poincare model - embeddings for graphs. - - """ - def _clear_post_train(self): - raise NotImplementedError() - - def _do_train_job(self, data_iterable, job_parameters, thread_private_mem): - raise NotImplementedError() - - def _set_train_params(self, **kwargs): - raise NotImplementedError() - def __init__(self, sentences=None, corpus_file=None, workers=3, vector_size=100, epochs=5, callbacks=(), batch_words=10000, trim_rule=None, sg=0, alpha=0.025, window=5, seed=1, hs=0, negative=5, ns_exponent=0.75, cbow_mean=1, min_alpha=0.0001, compute_loss=False, **kwargs): - """ + """Base class containing common methods for training, using & evaluating word embeddings learning models. Parameters ---------- @@ -713,6 +687,17 @@ def __init__(self, sentences=None, corpus_file=None, workers=3, vector_size=100, **kwargs : object Key word arguments needed to allow children classes to accept more arguments. + See Also + -------- + :class:`~gensim.models.word2vec.Word2Vec`. + Word2Vec model - embeddings for words. + :class:`~gensim.models.fasttext.FastText`. + FastText model - embeddings for words (ngram-based). + :class:`~gensim.models.doc2vec.Doc2Vec`. + Doc2Vec model - embeddings for documents. + :class:`~gensim.models.poincare.PoincareModel` + Poincare model - embeddings for graphs. + """ self.sg = int(sg) if vector_size % 4 != 0: @@ -753,6 +738,15 @@ def __init__(self, sentences=None, corpus_file=None, workers=3, vector_size=100, "and is not stored as part of the model. Model initialized without sentences. " "trim_rule provided, if any, will be ignored.") + def _clear_post_train(self): + raise NotImplementedError() + + def _do_train_job(self, data_iterable, job_parameters, thread_private_mem): + raise NotImplementedError() + + def _set_train_params(self, **kwargs): + raise NotImplementedError() + def __str__(self): """Get a human readable representation of the object. diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 537a1e7f94..299dd82d31 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -162,43 +162,11 @@ def count(self): class Doc2Vec(BaseWordEmbeddingsModel): - """Class for training, using and evaluating neural networks described in - `Distributed Representations of Sentences and Documents `_. - - Some important internal attributes are the following: - - Attributes - ---------- - wv : :class:`~gensim.models.keyedvectors.KeyedVectors` - This object essentially contains the mapping between words and embeddings. After training, it can be used - directly to query those embeddings in various ways. See the module level docstring for examples. - - docvecs : :class:`~gensim.models.keyedvectors.KeyedVectors` - This object contains the paragraph vectors learned from the training data. There will be one such vector - for each unique document tag supplied during training. They may be individually accessed using the tag - as an indexed-access key. For example, if one of the training documents used a tag of 'doc003': - - .. sourcecode:: pycon - - >>> model.docvecs['doc003'] - - vocabulary : :class:`~gensim.models.doc2vec.Doc2VecVocab` - This object represents the vocabulary (sometimes called Dictionary in gensim) of the model. - Besides keeping track of all unique words, this object provides extra functionality, such as - sorting words by frequency, or discarding extremely rare words. - - trainables : :class:`~gensim.models.doc2vec.Doc2VecTrainables` - This object represents the inner shallow neural network used to train the embeddings. The semantics of the - network differ slightly in the two available training modes (CBOW or SG) but you can think of it as a NN with - a single projection and hidden layer which we train on the corpus. The weights are then used as our embeddings - The only addition to the underlying NN used in :class:`~gensim.models.word2vec.Word2Vec` is that the input - includes not only the word vectors of each word in the context, but also the paragraph vector. - - """ def __init__(self, documents=None, corpus_file=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, callbacks=(), **kwargs): - """ + """Class for training, using and evaluating neural networks described in + `Distributed Representations of Sentences and Documents `_. Parameters ---------- @@ -287,6 +255,36 @@ def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_wo callbacks : :obj: `list` of :obj: `~gensim.models.callbacks.CallbackAny2Vec`, optional List of callbacks that need to be executed/run at specific stages during training. + Some important internal attributes are the following: + + Attributes + ---------- + wv : :class:`~gensim.models.keyedvectors.KeyedVectors` + This object essentially contains the mapping between words and embeddings. After training, it can be used + directly to query those embeddings in various ways. See the module level docstring for examples. + + docvecs : :class:`~gensim.models.keyedvectors.KeyedVectors` + This object contains the paragraph vectors learned from the training data. There will be one such vector + for each unique document tag supplied during training. They may be individually accessed using the tag + as an indexed-access key. For example, if one of the training documents used a tag of 'doc003': + + .. sourcecode:: pycon + + >>> model.docvecs['doc003'] + + vocabulary : :class:`~gensim.models.doc2vec.Doc2VecVocab` + This object represents the vocabulary (sometimes called Dictionary in gensim) of the model. + Besides keeping track of all unique words, this object provides extra functionality, such as + sorting words by frequency, or discarding extremely rare words. + + trainables : :class:`~gensim.models.doc2vec.Doc2VecTrainables` + This object represents the inner shallow neural network used to train the embeddings. The semantics + of the network differ slightly in the two available training modes (CBOW or SG) but you can think + of it as a NN with a single projection and hidden layer which we train on the corpus. The weights are + then used as our embeddings. The only addition to the underlying NN used in + :class:`~gensim.models.word2vec.Word2Vec` is that the input includes not only the word vectors of + each word in the context, but also the paragraph vector. + """ super(Doc2Vec, self).__init__( sg=(1 + dm) % 2, @@ -963,13 +961,10 @@ def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=N class Doc2VecVocab(Word2VecVocab): - """Vocabulary used by :class:`~gensim.models.doc2vec.Doc2Vec`. - - This includes a mapping from words found in the corpus to their total frequency count. - - """ def __init__(self, max_vocab_size=None, min_count=5, sample=1e-3, sorted_vocab=True, null_word=0, ns_exponent=0.75): - """ + """Vocabulary used by :class:`~gensim.models.doc2vec.Doc2Vec`. + + This includes a mapping from words found in the corpus to their total frequency count. Parameters ---------- @@ -1113,8 +1108,8 @@ def scan_vocab(self, documents=None, corpus_file=None, docvecs=None, progress_pe class Doc2VecTrainables(Word2VecTrainables): - """Represents the inner shallow neural network used to train :class:`~gensim.models.doc2vec.Doc2Vec`.""" def __init__(self, dm=1, dm_concat=0, dm_tag_count=1, vector_size=100, seed=1, hashfxn=hash, window=5): + """Represents the inner shallow neural network used to train :class:`~gensim.models.doc2vec.Doc2Vec`.""" super(Doc2VecTrainables, self).__init__( vector_size=vector_size, seed=seed, hashfxn=hashfxn) if dm and dm_concat: @@ -1146,10 +1141,8 @@ def reset_doc_weights(self, docvecs): class TaggedBrownCorpus(object): - """Reader for the `Brown corpus (part of NLTK data) `_.""" - def __init__(self, dirname): - """ + """Reader for the `Brown corpus (part of NLTK data) `_. Parameters ---------- @@ -1186,14 +1179,11 @@ def __iter__(self): class TaggedLineDocument(object): - """Iterate over a file that contains documents: one line = :class:`~gensim.models.doc2vec.TaggedDocument` object. - - Words are expected to be already preprocessed and separated by whitespace. Document tags are constructed - automatically from the document line number (each document gets a unique integer tag). - - """ def __init__(self, source): - """ + """Iterate over a file that contains documents: one line = :class:`~gensim.models.doc2vec.TaggedDocument` object. + + Words are expected to be already preprocessed and separated by whitespace. Document tags are constructed + automatically from the document line number (each document gets a unique integer tag). Parameters ---------- diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index e13651433a..ae6ea0870a 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -313,56 +313,33 @@ class FastText(BaseWordEmbeddingsModel): - """Train, use and evaluate word representations learned using the method - described in `Enriching Word Vectors with Subword Information `_, aka FastText. - - The model can be stored/loaded via its :meth:`~gensim.models.fasttext.FastText.save` and - :meth:`~gensim.models.fasttext.FastText.load` methods, or loaded from a format compatible with the original - Fasttext implementation via :func:`~gensim.models.fasttext.load_facebook_model`. - - Attributes - ---------- - wv : :class:`~gensim.models.fasttext.FastTextKeyedVectors` - This object essentially contains the mapping between words and embeddings. These are similar to the embeddings - computed in the :class:`~gensim.models.word2vec.Word2Vec`, however here we also include vectors for n-grams. - This allows the model to compute embeddings even for **unseen** words (that do not exist in the vocabulary), - as the aggregate of the n-grams included in the word. After training the model, this attribute can be used - directly to query those embeddings in various ways. Check the module level docstring for some examples. - vocabulary : :class:`~gensim.models.fasttext.FastTextVocab` - This object represents the vocabulary of the model. - Besides keeping track of all unique words, this object provides extra functionality, such as - constructing a huffman tree (frequent words are closer to the root), or discarding extremely rare words. - trainables : :class:`~gensim.models.fasttext.FastTextTrainables` - This object represents the inner shallow neural network used to train the embeddings. This is very - similar to the network of the :class:`~gensim.models.word2vec.Word2Vec` model, but it also trains weights - for the N-Grams (sequences of more than 1 words). The semantics of the network are almost the same as - the one used for the :class:`~gensim.models.word2vec.Word2Vec` model. - You can think of it as a NN with a single projection and hidden layer which we train on the corpus. - The weights are then used as our embeddings. An important difference however between the two models, is the - scoring function used to compute the loss. In the case of FastText, this is modified in word to also account - for the internal structure of words, besides their concurrence counts. - - """ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, word_ngrams=1, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, bucket=2000000, trim_rule=None, batch_words=MAX_WORDS_IN_BATCH, callbacks=(), compatible_hash=True): - """ + """Train, use and evaluate word representations learned using the method + described in `Enriching Word Vectors with Subword Information `_, + aka FastText. + + The model can be stored/loaded via its :meth:`~gensim.models.fasttext.FastText.save` and + :meth:`~gensim.models.fasttext.FastText.load` methods, or loaded from a format compatible with the + original Fasttext implementation via :func:`~gensim.models.fasttext.load_facebook_model`. Parameters ---------- sentences : iterable of list of str, optional Can be simply a list of lists of tokens, but for larger corpora, consider an iterable 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` in :mod:`~gensim.models.word2vec` module for such examples. - If you don't supply `sentences`, the model is left uninitialized -- use if you plan to initialize it - in some other way. + See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus' + or :class:`~gensim.models.word2vec.LineSentence` in :mod:`~gensim.models.word2vec` module for such + examples. If you don't supply `sentences`, the model is left uninitialized -- use if you plan to + initialize it in some other way. corpus_file : str, optional Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or - `corpus_file` arguments need to be passed (or none of them, in that case, the model is left uninitialized). + `corpus_file` arguments need to be passed (or none of them, in that case, the model is left + uninitialized). min_count : int, optional The model ignores all words with total frequency lower than this. size : int, optional @@ -462,6 +439,29 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, size=100, alpha >>> say_vector = model.wv['say'] # get vector for word >>> of_vector = model.wv['of'] # get vector for out-of-vocab word + Attributes + ---------- + wv : :class:`~gensim.models.fasttext.FastTextKeyedVectors` + This object essentially contains the mapping between words and embeddings. These are similar to + the embedding computed in the :class:`~gensim.models.word2vec.Word2Vec`, however here we also + include vectors for n-grams. This allows the model to compute embeddings even for **unseen** + words (that do not exist in the vocabulary), as the aggregate of the n-grams included in the word. + After training the model, this attribute can be used directly to query those embeddings in various + ways. Check the module level docstring for some examples. + vocabulary : :class:`~gensim.models.fasttext.FastTextVocab` + This object represents the vocabulary of the model. + Besides keeping track of all unique words, this object provides extra functionality, such as + constructing a huffman tree (frequent words are closer to the root), or discarding extremely rare words. + trainables : :class:`~gensim.models.fasttext.FastTextTrainables` + This object represents the inner shallow neural network used to train the embeddings. This is very + similar to the network of the :class:`~gensim.models.word2vec.Word2Vec` model, but it also trains weights + for the N-Grams (sequences of more than 1 words). The semantics of the network are almost the same as + the one used for the :class:`~gensim.models.word2vec.Word2Vec` model. + You can think of it as a NN with a single projection and hidden layer which we train on the corpus. + The weights are then used as our embeddings. An important difference however between the two models, is the + scoring function used to compute the loss. In the case of FastText, this is modified in word to also account + for the internal structure of words, besides their concurrence counts. + """ self.load = call_on_class_only self.load_fasttext_format = call_on_class_only @@ -1255,55 +1255,55 @@ def save_facebook_model(model, path, encoding="utf-8", lr_update_rate=100, word_ class FastTextKeyedVectors(KeyedVectors): - """Vectors and vocab for :class:`~gensim.models.fasttext.FastText`. - - Implements significant parts of the FastText algorithm. For example, - the :func:`word_vec` calculates vectors for out-of-vocabulary (OOV) - entities. FastText achieves this by keeping vectors for ngrams: - adding the vectors for the ngrams of an entity yields the vector for the - entity. + def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): + """Vectors and vocab for :class:`~gensim.models.fasttext.FastText`. - Similar to a hashmap, this class keeps a fixed number of buckets, and - maps all ngrams to buckets using a hash function. + Implements significant parts of the FastText algorithm. For example, + the :func:`word_vec` calculates vectors for out-of-vocabulary (OOV) + entities. FastText achieves this by keeping vectors for ngrams: + adding the vectors for the ngrams of an entity yields the vector for the + entity. - This class also provides an abstraction over the hash functions used by - Gensim's FastText implementation over time. The hash function connects - ngrams to buckets. Originally, the hash function was broken and - incompatible with Facebook's implementation. The current hash is fully - compatible. + Similar to a hashmap, this class keeps a fixed number of buckets, and + maps all ngrams to buckets using a hash function. - Parameters - ---------- - vector_size : int - The dimensionality of all vectors. - min_n : int - The minimum number of characters in an ngram - max_n : int - The maximum number of characters in an ngram - bucket : int - The number of buckets. - compatible_hash : boolean - If True, uses the Facebook-compatible hash function instead of the - Gensim backwards-compatible hash function. + This class also provides an abstraction over the hash functions used by + Gensim's FastText implementation over time. The hash function connects + ngrams to buckets. Originally, the hash function was broken and + incompatible with Facebook's implementation. The current hash is fully + compatible. - Attributes - ---------- - vectors_vocab : np.array - Each row corresponds to a vector for an entity in the vocabulary. - Columns correspond to vector dimensions. When embedded in a full - FastText model, these are the full-word-token vectors updated - by training, whereas the inherited vectors are the actual per-word - vectors synthesized from the full-word-token and all subword (ngram) - vectors. - vectors_ngrams : np.array - A vector for each ngram across all entities in the vocabulary. - Each row is a vector that corresponds to a bucket. - Columns correspond to vector dimensions. - buckets_word : dict - Maps vocabulary items (by their index) to the buckets they occur in. + Parameters + ---------- + vector_size : int + The dimensionality of all vectors. + min_n : int + The minimum number of characters in an ngram + max_n : int + The maximum number of characters in an ngram + bucket : int + The number of buckets. + compatible_hash : boolean + If True, uses the Facebook-compatible hash function instead of the + Gensim backwards-compatible hash function. + + Attributes + ---------- + vectors_vocab : np.array + Each row corresponds to a vector for an entity in the vocabulary. + Columns correspond to vector dimensions. When embedded in a full + FastText model, these are the full-word-token vectors updated + by training, whereas the inherited vectors are the actual per-word + vectors synthesized from the full-word-token and all subword (ngram) + vectors. + vectors_ngrams : np.array + A vector for each ngram across all entities in the vocabulary. + Each row is a vector that corresponds to a bucket. + Columns correspond to vector dimensions. + buckets_word : dict + Maps vocabulary items (by their index) to the buckets they occur in. - """ - def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): + """ super(FastTextKeyedVectors, self).__init__(vector_size=vector_size) self.vectors_vocab = None # fka syn0_vocab self.vectors_ngrams = None # fka syn0_ngrams diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 839ae463dc..fb07e4a36a 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -188,13 +188,13 @@ class KeyedVectors(utils.SaveLoad): - """Abstract base class / interface for various types of word vectors.""" - """Class containing common methods for operations over word vectors.""" - """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 __init__(self, vector_size, mapfile_path=None): + """Mapping between keys (such as words) and vectors for :class:`~gensim.models.Word2Vec` + and related models. - """ - def __init__(self, vector_size): + Used to perform operations on the vectors such as vector lookup, distance, similarity etc. + + """ self.vectors = zeros((0, vector_size), dtype=REAL) # fka (formerly known as) syn0 self.vectors_norm = None # fka syn0norm self.map = {} @@ -1378,11 +1378,11 @@ def _l2_norm(m, replace=False): 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): + """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). + + """ self.count = 0 self.__dict__.update(kwargs) diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 9129c84cac..88d41b09ff 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -227,45 +227,23 @@ def score_cbow_pair(model, word, l1): class Word2Vec(BaseWordEmbeddingsModel): - """Train, use and evaluate neural networks described in https://code.google.com/p/word2vec/. - - Once you're finished training a model (=no more updates, only querying) - store and use only the :class:`~gensim.models.keyedvectors.KeyedVectors` instance in `self.wv` to reduce memory. - - The model can be stored/loaded via its :meth:`~gensim.models.word2vec.Word2Vec.save` and - :meth:`~gensim.models.word2vec.Word2Vec.load` methods. - - The trained word vectors can also be stored/loaded from a format compatible with the - original word2vec implementation via `self.wv.save_word2vec_format` - and :meth:`gensim.models.keyedvectors.KeyedVectors.load_word2vec_format`. - - Some important attributes are the following: - - Attributes - ---------- - wv : :class:`~gensim.models.keyedvectors.KeyedVectors` - This object essentially contains the mapping between words and embeddings. After training, it can be used - directly to query those embeddings in various ways. See the module level docstring for examples. - - vocabulary : :class:`~gensim.models.word2vec.Word2VecVocab` - This object represents the vocabulary (sometimes called Dictionary in gensim) of the model. - Besides keeping track of all unique words, this object provides extra functionality, such as - constructing a huffman tree (frequent words are closer to the root), or discarding extremely rare words. - - trainables : :class:`~gensim.models.word2vec.Word2VecTrainables` - This object represents the inner shallow neural network used to train the embeddings. The semantics of the - network differ slightly in the two available training modes (CBOW or SG) but you can think of it as a NN with - a single projection and hidden layer which we train on the corpus. The weights are then used as our embeddings - (which means that the size of the hidden layer is equal to the number of features `self.size`). - - """ - def __init__(self, sentences=None, corpus_file=None, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, trim_rule=None, sorted_vocab=1, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False, callbacks=(), max_final_vocab=None): - """ + """Train, use and evaluate neural networks described in https://code.google.com/p/word2vec/. + + Once you're finished training a model (=no more updates, only querying) + store and use only the :class:`~gensim.models.keyedvectors.KeyedVectors` instance in `self.wv` + to reduce memory. + + The full model can be stored/loaded via its :meth:`~gensim.models.word2vec.Word2Vec.save` and + :meth:`~gensim.models.word2vec.Word2Vec.load` methods. + + The trained word vectors can also be stored/loaded from a format compatible with the + original word2vec implementation via `self.wv.save_word2vec_format` + and :meth:`gensim.models.keyedvectors.KeyedVectors.load_word2vec_format`. Parameters ---------- @@ -368,6 +346,26 @@ def __init__(self, sentences=None, corpus_file=None, size=100, alpha=0.025, wind >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] >>> model = Word2Vec(sentences, min_count=1) + Some important attributes are the following: + + Attributes + ---------- + wv : :class:`~gensim.models.keyedvectors.KeyedVectors` + This object essentially contains the mapping between words and embeddings. After training, it can be used + directly to query those embeddings in various ways. See the module level docstring for examples. + + vocabulary : :class:`~gensim.models.word2vec.Word2VecVocab` + This object represents the vocabulary (sometimes called Dictionary in gensim) of the model. + Besides keeping track of all unique words, this object provides extra functionality, such as + constructing a huffman tree (frequent words are closer to the root), or discarding extremely rare words. + + trainables : :class:`~gensim.models.word2vec.Word2VecTrainables` + This object represents the inner shallow neural network used to train the embeddings. The semantics + of the network differ slightly in the two available training modes (CBOW or SG) but you can think of it + as a NN with single projection and hidden layer which we train on the corpus. The weights are then used + as our embeddings (which means that the size of the hidden layer is equal to the number of features + `self.size`). + """ self.max_final_vocab = max_final_vocab @@ -851,11 +849,11 @@ def load(cls, *args, **kwargs): class BrownCorpus(object): - """Iterate over sentences from the `Brown corpus `_ - (part of `NLTK data `_). - - """ def __init__(self, dirname): + """Iterate over sentences from the `Brown corpus `_ + (part of `NLTK data `_). + + """ self.dirname = dirname def __iter__(self): @@ -877,8 +875,8 @@ def __iter__(self): class Text8Corpus(object): - """Iterate over sentences from the "text8" corpus, unzipped from http://mattmahoney.net/dc/text8.zip.""" def __init__(self, fname, max_sentence_length=MAX_WORDS_IN_BATCH): + """Iterate over sentences from the "text8" corpus, unzipped from http://mattmahoney.net/dc/text8.zip.""" self.fname = fname self.max_sentence_length = max_sentence_length @@ -905,12 +903,9 @@ def __iter__(self): class LineSentence(object): - """Iterate over a file that contains sentences: one line = one sentence. - Words must be already preprocessed and separated by whitespace. - - """ def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None): - """ + """Iterate over a file that contains sentences: one line = one sentence. + Words must be already preprocessed and separated by whitespace. Parameters ---------- @@ -957,22 +952,20 @@ def __iter__(self): class PathLineSentences(object): - """Like :class:`~gensim.models.word2vec.LineSentence`, but process all files in a directory - in alphabetical order by filename. + def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None): + """Like :class:`~gensim.models.word2vec.LineSentence`, but process all files in a directory + in alphabetical order by filename. - The directory must only contain files that can be read by :class:`gensim.models.word2vec.LineSentence`: - .bz2, .gz, and text files. Any file not ending with .bz2 or .gz is assumed to be a text file. + The directory must only contain files that can be read by :class:`gensim.models.word2vec.LineSentence`: + .bz2, .gz, and text files. Any file not ending with .bz2 or .gz is assumed to be a text file. - The format of files (either text, or compressed text files) in the path is one sentence = one line, - with words already preprocessed and separated by whitespace. + The format of files (either text, or compressed text files) in the path is one sentence = one line, + with words already preprocessed and separated by whitespace. - Warnings - -------- - Does **not recurse** into subdirectories. + Warnings + -------- + Does **not recurse** into subdirectories. - """ - def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None): - """ Parameters ---------- source : str @@ -1047,10 +1040,10 @@ def _scan_vocab_worker(stream, progress_queue, max_vocab_size=None, trim_rule=No class Word2VecVocab(utils.SaveLoad): - """Vocabulary used by :class:`~gensim.models.word2vec.Word2Vec`.""" def __init__( self, max_vocab_size=None, min_count=5, sample=1e-3, sorted_vocab=True, null_word=0, max_final_vocab=None, ns_exponent=0.75): + """Vocabulary used by :class:`~gensim.models.word2vec.Word2Vec`.""" self.max_vocab_size = max_vocab_size self.min_count = min_count self.sample = sample @@ -1372,8 +1365,8 @@ def _assign_binary_codes(vocab): class Word2VecTrainables(utils.SaveLoad): - """Represents the inner shallow neural network used to train :class:`~gensim.models.word2vec.Word2Vec`.""" def __init__(self, vector_size=100, seed=1, hashfxn=hash): + """Represents the inner shallow neural network used to train :class:`~gensim.models.word2vec.Word2Vec`.""" self.hashfxn = hashfxn self.layer1_size = vector_size self.seed = seed From cd02b8b87de1b3ddeda17b6bf939cb3138ec57d9 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 17 Dec 2019 11:16:27 -0800 Subject: [PATCH 10/60] name/comment harmonization (rm 'entity', lessen 'word'-centricity) --- gensim/models/keyedvectors.py | 257 ++++++++++++++++++++-------------- 1 file changed, 148 insertions(+), 109 deletions(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index fb07e4a36a..cbddd658a8 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -5,20 +5,25 @@ # 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. +"""This module implements word vectors, and more generally sets of vectors keyed by lookup tokens/ints, + and various 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 structure is called "KeyedVectors" and is essentially a mapping between *keys* +and *vectors*. Each vector is identified by its lookup key, most often a short string token, so this is usually +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, the 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. +The key is, in the original motivating case, a word (so the mapping maps words to 1D vectors), +but for some models, the key can also correspond to a document, a graph node etc. + +(Because some applications may maintain their own integral identifiers, compact and contiguous +starting at zero, this class also supports use of plain ints as keys – in that case using them as literal +pointers to the position of the desired vector in the underlying array, and saving the overhead of +a lookup map entry.) Why use KeyedVectors instead of a full model? ============================================= @@ -35,7 +40,7 @@ | 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. | +| append new vectors | ✅ | ✅ | Add new-vector entries to the mapping dynamically. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | concurrency | ✅ | ✅ | Thread-safe, allows concurrent vector queries. | +---------------------------+--------------+------------+-------------------------------------------------------------+ @@ -169,13 +174,14 @@ from Queue import Queue, Empty # noqa:F401 from numpy import dot, float32 as REAL, \ - double, array, zeros, vstack, sqrt, newaxis, integer, \ + double, array, zeros, vstack, sqrt, newaxis, \ ndarray, sum as np_sum, prod, argmax, dtype, ascontiguousarray, \ frombuffer import numpy as np from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc from gensim.corpora.dictionary import Dictionary +from gensim.utils import deprecated from six import string_types, integer_types from six.moves import zip, range from scipy import stats @@ -187,6 +193,9 @@ logger = logging.getLogger(__name__) +KEY_TYPES = (string_types, integer_types, np.integer) + + class KeyedVectors(utils.SaveLoad): def __init__(self, vector_size, mapfile_path=None): """Mapping between keys (such as words) and vectors for :class:`~gensim.models.Word2Vec` @@ -203,6 +212,7 @@ def __init__(self, vector_size, mapfile_path=None): self.mapfile_path = mapfile_path def _load_specials(self, *args, **kwargs): + """Handle special requirements of `.load()` protocol, usually up-converting older versions.""" super(KeyedVectors, self)._load_specials(*args, **kwargs) if hasattr(self, 'doctags'): self._upconvert_old_d2vkv() @@ -214,7 +224,7 @@ def _load_specials(self, *args, **kwargs): self.map = self.__dict__.pop('vocab', None) def resize_vectors(self): - """Make vectors match index2key size""" + """Make underlying vectors match index2key size.""" target_count = len(self.index2key) prev_count = len(self.vectors) if prev_count == target_count: @@ -229,6 +239,10 @@ def resize_vectors(self): return range(prev_count, target_count) def randomly_initialize_vectors(self, indexes=None, seed=0): + """Initialize vectors with low-magnitude random vectors, as is typical for pre-trained + Word2Vec and related models. + + """ if indexes is None: indexes = range(0, len(self.vectors)) for i in indexes: @@ -253,13 +267,16 @@ def __getitem__(self, key_or_keys): Vector representation for `key_or_keys` (1D if `key_or_keys` is single key, otherwise - 2D). """ - if isinstance(key_or_keys, (string_types, integer_types, np.integer)): + if isinstance(key_or_keys, KEY_TYPES): return self.get_vector(key_or_keys) return vstack([self.get_vector(key) for key in key_or_keys]) def get_index(self, key): - """TODO comment""" + """Return the integer index (slot/position) where the given key's vector is stored in the + backing vectors array. + + """ if key in self.map: return self.map[key].index elif isinstance(key, (integer_types, np.integer)) and key < len(self.vectors): @@ -268,7 +285,7 @@ def get_index(self, key): raise KeyError("Key '%s' not in vocabulary" % key) def get_vector(self, key, use_norm=False): - """Get the entity's representations in vector space, as a 1D numpy array. + """Get the key's vector, as a 1D numpy array. Parameters ---------- @@ -301,67 +318,74 @@ def word_vec(self, *args, **kwargs): """Compatibility alias for get_vector()""" return self.get_vector(*args, **kwargs) - 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. + def add(self, keys, weights, replace=False): + """Append keys and theirs vectors in a manual way. + If some key 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. + keys : list of (str or int) + keys specified by string or int ids. weights: list of numpy.ndarray or 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, + Flag indicating whether to replace vectors for keys which already exist in the map if True - replace vectors, otherwise - keep old vectors. """ - if isinstance(entities, string_types): - entities = [entities] + if isinstance(keys, KEY_TYPES): + keys = [keys] 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 = np.zeros(len(keys), dtype=np.bool) + for idx, key in enumerate(keys): + if key in self: 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.index2key.append(entity) + key = keys[idx] + self.map[key] = Vocab(index=len(self.index2key), count=1) + self.index2key.append(key) # add vectors for new entities self.vectors = vstack((self.vectors, weights[~in_vocab_mask].astype(self.vectors.dtype))) # 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]] + in_vocab_idxs = [self.map[keys[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. + def __setitem__(self, keys, weights): + """Add keys and theirs vectors in a manual way. + If some key is already in the vocabulary, old vector is replaced with the new one. This method is alias for :meth:`~gensim.models.keyedvectors.KeyedVectors.add` with `replace=True`. Parameters ---------- - entities : {str, list of str} - Entities specified by their string ids. + keys : {str, int, list of (str or int)} + keys specified by their string or int ids. weights: list of numpy.ndarray or numpy.ndarray List of 1D np.array vectors or 2D np.array of vectors. """ - if not isinstance(entities, list): - entities = [entities] + if not isinstance(keys, list): + keys = [keys] weights = weights.reshape(1, -1) - self.add(entities, weights, replace=True) + self.add(keys, weights, replace=True) def has_index_for(self, key): - """Can this model return an index for this key?""" + """Can this model return a single index for this key? + + Subclasses that synthesize vectors for out-of-vocabulary words (like + :class:`~gensim.models.fasttext.FastText`) may respond True for a + simple `word in wv` (`__contains__()`) check but False for this + more-specific check. + + """ try: return self.get_index(key) >= 0 except KeyError: @@ -370,24 +394,25 @@ def has_index_for(self, key): def __contains__(self, key): return self.has_index_for(key) - 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 most_similar_to_given(self, key1, keys_list): + """Get the `key` from `keys_list` most similar to `key1`.""" + return keys_list[argmax([self.similarity(key1, key) for key in keys_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 + def closer_than(self, key1, key2): + """Get all keys that are closer to `key1` than `key2` is to `key1`.""" + all_distances = self.distances(key1) + e1_index = self.vocab[key1].index + e2_index = self.vocab[key2].index closer_node_indices = np.where(all_distances < all_distances[e2_index])[0] return [self.index2key[index] for index in closer_node_indices if index != e1_index] + @deprecated("Use closer_than instead") def words_closer_than(self, word1, word2): return self.closer_than(word1, word2) - 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 + def rank(self, key1, key2): + """Rank of the distance of `key2` from `key1`, in relation to distances of all keys from `key1`.""" + return len(self.closer_than(key1, key2)) + 1 # backward compatibility @property @@ -434,23 +459,23 @@ def save(self, *args, **kwargs): def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip_end=None, restrict_vocab=None, indexer=None): - """Find the top-N most similar words. - Positive words contribute positively towards the similarity, negative words negatively. + """Find the top-N most similar keys. + Positive keys contribute positively towards the similarity, negative keys 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. + weight vectors of the given keys and the vectors for each key 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. + positive : list of (str or int or ndarray), optional + List of keys that contribute positively. + negative : list of (str or int or ndarray), optional + List of keys that contribute negatively. topn : int or None, optional - Number of top-N similar words to return, when `topn` is int. When `topn` is None, - then similarities for all words are returned. + Number of top-N similar keys to return, when `topn` is int. When `topn` is None, + then similarities for all keys are returned. clip_start : int Start clipping index. clip_end : int @@ -458,15 +483,15 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip 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 + only check the first 10000 key vectors in the vocabulary order. (This may be meaningful if you've sorted the vocabulary by descending frequency.) If specified, overrides any values of clip_start or clip_end Returns ------- list of (str, float) or numpy.array - When `topn` is int, a sequence of (word, similarity) is returned. - When `topn` is None, then similarities for all words are returned as a + When `topn` is int, a sequence of (key, similarity) is returned. + When `topn` is None, then similarities for all keys are returned as a one-dimensional numpy array with the size of the vocabulary. """ @@ -485,29 +510,29 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip clip_start = 0 clip_end = restrict_vocab - if isinstance(positive, string_types + integer_types + (integer,)) and not negative: + if isinstance(positive, KEY_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 + # add weights for each key, if not already present; default to 1.0 for positive and -1.0 for negative keys positive = [ - (key, 1.0) if isinstance(key, string_types + integer_types + (ndarray, integer)) - else key for key in positive + (item, 1.0) if isinstance(item, KEY_TYPES + (ndarray,)) + else item for item in positive ] negative = [ - (key, -1.0) if isinstance(key, string_types + integer_types + (ndarray, integer)) - else key for key in negative + (item, -1.0) if isinstance(item, KEY_TYPES + (ndarray,)) + else item for item 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) + # compute the weighted average of all keys + all_keys, mean = set(), [] + for key, weight in positive + negative: + if isinstance(key, ndarray): + mean.append(weight * key) else: - mean.append(weight * self.word_vec(word, use_norm=True)) - if self.has_index_for(word): - all_words.add(self.get_index(word)) + mean.append(weight * self.get_vector(key, use_norm=True)) + if self.has_index_for(key): + all_keys.add(self.get_index(key)) if not mean: raise ValueError("cannot compute similarity with no input") mean = matutils.unitvec(array(mean).mean(axis=0)).astype(REAL) @@ -518,59 +543,63 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip dists = dot(self.vectors_norm[clip_start:clip_end], 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 + best = matutils.argsort(dists, topn=topn + len(all_keys), reverse=True) + # ignore (don't return) keys from the input result = [(self.index2key[sim + clip_start], float(dists[sim])) - for sim in best if (sim + clip_start) not in all_words] + for sim in best if (sim + clip_start) not in all_keys] return result[:topn] def similar_by_word(self, word, topn=10, restrict_vocab=None): - """Find the top-N most similar words. + """Compatibility alias for similar_by_key()""" + return self.similar_by_key(word, topn, restrict_vocab) + + def similar_by_key(self, key, topn=10, restrict_vocab=None): + """Find the top-N most similar keys. Parameters ---------- - word : str - Word + key : str + Key topn : int or None, optional - Number of top-N similar words to return. If topn is None, similar_by_word returns + Number of top-N similar keys to return. If topn is None, similar_by_key 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 + only check the first 10000 key vectors in the vocabulary order. (This may be meaningful if you've sorted the vocabulary by descending frequency.) Returns ------- list of (str, float) or numpy.array - When `topn` is int, a sequence of (word, similarity) is returned. - When `topn` is None, then similarities for all words are returned as a + When `topn` is int, a sequence of (key, similarity) is returned. + When `topn` is None, then similarities for all keys are returned as a one-dimensional numpy array with the size of the vocabulary. """ - return self.most_similar(positive=[word], topn=topn, restrict_vocab=restrict_vocab) + return self.most_similar(positive=[key], 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. + """Find the top-N most similar keys by vector. Parameters ---------- vector : numpy.array Vector from which similarities are to be computed. topn : int or None, optional - Number of top-N similar words to return, when `topn` is int. When `topn` is None, - then similarities for all words are returned. + Number of top-N similar keys to return, when `topn` is int. When `topn` is None, + then similarities for all keys are returned. 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 + only check the first 10000 key vectors in the vocabulary order. (This may be meaningful if you've sorted the vocabulary by descending frequency.) Returns ------- list of (str, float) or numpy.array - When `topn` is int, a sequence of (word, similarity) is returned. - When `topn` is None, then similarities for all words are returned as a + When `topn` is int, a sequence of (key, similarity) is returned. + When `topn` is None, then similarities for all keys are returned as a one-dimensional numpy array with the size of the vocabulary. """ @@ -709,6 +738,7 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): When `topn` is None, then similarities for all words are returned as a one-dimensional numpy array with the size of the vocabulary. + # TODO: Update to better match & share code with most_similar() """ if isinstance(topn, Integral) and topn < 1: return [] @@ -755,17 +785,17 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): return result[:topn] def doesnt_match(self, words): - """Which word from the given list doesn't go with the others? + """Which key from the given list doesn't go with the others? Parameters ---------- words : list of str - List of words. + List of keys. Returns ------- str - The word further away from the mean of all words. + The key further away from the mean of all keys. """ self.init_sims() @@ -827,7 +857,7 @@ def distances(self, word_or_vector, other_words=()): If either `word_or_vector` or any word in `other_words` is absent from vocab. """ - if isinstance(word_or_vector, string_types): + if isinstance(word_or_vector, KEY_TYPES): input_vector = self.word_vec(word_or_vector) else: input_vector = word_or_vector @@ -839,15 +869,15 @@ def distances(self, word_or_vector, other_words=()): return 1 - self.cosine_similarities(input_vector, other_vectors) def distance(self, w1, w2): - """Compute cosine distance between two words. + """Compute cosine distance between two keys. Calculate 1 - :meth:`~gensim.models.keyedvectors.KeyedVectors.similarity`. Parameters ---------- w1 : str - Input word. + Input key. w2 : str - Input word. + Input key. Returns ------- @@ -858,14 +888,14 @@ def distance(self, w1, w2): return 1 - self.similarity(w1, w2) def similarity(self, w1, w2): - """Compute cosine similarity between two words. + """Compute cosine similarity between two keys. Parameters ---------- w1 : str - Input word. + Input key. w2 : str - Input word. + Input key. Returns ------- @@ -876,14 +906,14 @@ def similarity(self, w1, 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. + """Compute cosine similarity between two sets of keys. Parameters ---------- ws1 : list of str - Sequence of words. + Sequence of keys. ws2: list of str - Sequence of words. + Sequence of keys. Returns ------- @@ -893,8 +923,8 @@ def n_similarity(self, ws1, 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] + v1 = [self[key] for key in ws1] + v2 = [self[key] for key in ws2] return dot(matutils.unitvec(array(v1).mean(axis=0)), matutils.unitvec(array(v2).mean(axis=0))) @staticmethod @@ -1163,7 +1193,7 @@ def init_sims(self, replace=False): """ if getattr(self, 'vectors_norm', None) is None or replace: - logger.info("precomputing L2-norms of word weight vectors") + logger.info("precomputing L2-norms of key weight vectors") self.vectors_norm = _l2_norm(self.vectors, replace=replace) def relative_cosine_similarity(self, wa, wb, topn=10): @@ -1545,6 +1575,7 @@ def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8' def load_word2vec_format(*args, **kwargs): + """Alias for `KeyedVectors.load_word2vec_format(...)`""" return KeyedVectors.load_word2vec_format(*args, **kwargs) @@ -1564,6 +1595,16 @@ def pseudorandom_weak_vector(size, seed_string=None, hashfxn=hash): class ConcatList(UserList): + """Pseudo-list that stitches together multiple underlying sequences, but + only offers indexed-access and iteration. + + (Used to support KeyedVectors optimization in case of mixed plain-int and + string keys, where all plain-int keys are represented by a simple `range()` + object, followed by a real list.) + + # TODO: implement or stub as NotImplemented other necessary methods, + # especially slicing? + """ def __getitem__(self, index): for subseq in self.data: if index >= len(subseq): @@ -1578,5 +1619,3 @@ def __iter__(self): def __len__(self): return sum(len(subseq) for subseq in self.data) - -# TODO: implement or stub as NotImplemented other methods From 0c77ae4959f70bd14e69185b4811578904368cf6 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 17 Dec 2019 11:53:20 -0800 Subject: [PATCH 11/60] table formatting --- gensim/models/keyedvectors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index cbddd658a8..723c99eb9a 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -40,7 +40,7 @@ | fasttext/word2vec format | ✅ | ❌ | do not support further training, but you can still load | | | | | them into KeyedVectors. | +---------------------------+--------------+------------+-------------------------------------------------------------+ -| append new vectors | ✅ | ✅ | Add new-vector entries to the mapping dynamically. | +| append new vectors | ✅ | ✅ | Add new-vector entries to the mapping dynamically. | +---------------------------+--------------+------------+-------------------------------------------------------------+ | concurrency | ✅ | ✅ | Thread-safe, allows concurrent vector queries. | +---------------------------+--------------+------------+-------------------------------------------------------------+ From cfa723dc9af431b5853f4ed000ee509268d14423 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 17 Dec 2019 11:58:06 -0800 Subject: [PATCH 12/60] return pyemd to linux test env --- setup.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 02348c7d68..65b2324333 100644 --- a/setup.py +++ b/setup.py @@ -253,13 +253,12 @@ def run(self): distributed_env = ['Pyro4 >= 4.27'] -win_testenv = [ +linux_testenv = [ 'pytest', 'pytest-rerunfailures', 'mock', 'cython', 'nmslib', - 'pyemd', 'testfixtures', 'Morfessor==2.0.2a4', 'python-Levenshtein >= 0.10.2', @@ -269,8 +268,13 @@ def run(self): # See https://github.com/RaRe-Technologies/gensim/pull/2814 # 'tensorflow', # 'keras', + 'pyemd', # see below; keep as last until appveyor issue resolved ] +# temporarily remove pyemd to work around appveyor issues +win_testenv = linux_testenv[:-1] + +# # This list partially duplicates requirements_docs.txt. # The main difference is that we don't include version pins here unless # absolutely necessary, whereas requirements_docs.txt includes pins for From a4f7b77d0d013d071941505291790e84c3650375 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 17 Dec 2019 16:41:14 -0800 Subject: [PATCH 13/60] split backcompat tests for better resolution --- gensim/test/test_doc2vec.py | 74 +++++++++++++++++++++++----------- gensim/test/test_word2vec.py | 78 ++++++++++++++++++++++++++---------- 2 files changed, 108 insertions(+), 44 deletions(-) diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index f255d612e6..5bad1d8539 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -103,9 +103,9 @@ def testPersistenceWord2VecFormat(self): self.assertEqual(len(model.wv.vocab), len(binary_model_dv.vocab)) def testLoadOldModel(self): - """Test loading doc2vec models from previous version""" + """Test loading an old doc2vec model from indeterminate version""" - model_file = 'doc2vec_old' + model_file = 'doc2vec_old' # which version?!? model = doc2vec.Doc2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (3955, 100)) self.assertTrue(len(model.wv.vocab) == 3955) @@ -121,6 +121,9 @@ def testLoadOldModel(self): self.model_sanity(model) + def testLoadOldModelSeparates(self): + """Test loading an old doc2vec model from indeterminate version""" + # Model stored in multiple files model_file = 'doc2vec_old_sep' model = doc2vec.Doc2Vec.load(datapath(model_file)) @@ -137,38 +140,63 @@ def testLoadOldModel(self): self.model_sanity(model) - # load really old model + def test_load_old_models_pre_1_0(self): + """Test loading pre-1.0 models""" model_file = 'd2v-lee-v0.13.0' model = doc2vec.Doc2Vec.load(datapath(model_file)) self.model_sanity(model) - # Test loading doc2vec models from all previous versions old_versions = [ '0.12.0', '0.12.1', '0.12.2', '0.12.3', '0.12.4', '0.13.0', '0.13.1', '0.13.2', '0.13.3', '0.13.4', - '1.0.0', '1.0.1', '2.0.0', '2.1.0', '2.2.0', '2.3.0', + ] + for old_version in old_versions: + self._check_old_version(old_version) + + def test_load_old_models_1_x(self): + """Test loading 1.x models""" + old_versions = [ + '1.0.0', '1.0.1', + ] + for old_version in old_versions: + self._check_old_version(old_version) + + def test_load_old_models_2_x(self): + """Test loading 2.x models""" + old_versions = [ + '2.0.0', '2.1.0', '2.2.0', '2.3.0', + ] + for old_version in old_versions: + self._check_old_version(old_version) + + def test_load_old_models_3_x(self): + """Test loading 3.x models""" + old_versions = [ '3.0.0', '3.1.0', '3.2.0', '3.3.0', '3.4.0' ] + for old_version in old_versions: + self._check_old_version(old_version) + def _check_old_version(self, old_version): + logging.info("TESTING LOAD of %s Doc2Vec MODEL", old_version) saved_models_dir = datapath('old_d2v_models/d2v_{}.mdl') - for old_version in old_versions: - model = doc2vec.Doc2Vec.load(saved_models_dir.format(old_version)) - self.assertTrue(len(model.wv.vocab) == 3) - self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.wv.vectors.shape == (3, 4)) - self.assertTrue(model.docvecs.vectors.shape == (2, 4)) - self.assertTrue(len(model.docvecs) == 2) - # check if inferring vectors for new documents and similarity search works. - doc0_inferred = model.infer_vector(list(DocsLeeCorpus())[0].words) - sims_to_infer = model.docvecs.most_similar([doc0_inferred], topn=len(model.docvecs)) - self.assertTrue(sims_to_infer) - # check if inferring vectors and similarity search works after saving and loading back the model - tmpf = get_tmpfile('gensim_doc2vec.tst') - model.save(tmpf) - loaded_model = doc2vec.Doc2Vec.load(tmpf) - doc0_inferred = loaded_model.infer_vector(list(DocsLeeCorpus())[0].words) - sims_to_infer = loaded_model.docvecs.most_similar([doc0_inferred], topn=len(loaded_model.docvecs)) - self.assertTrue(sims_to_infer) + model = doc2vec.Doc2Vec.load(saved_models_dir.format(old_version)) + self.assertTrue(len(model.wv.vocab) == 3) + self.assertIsNone(model.corpus_total_words) + self.assertTrue(model.wv.vectors.shape == (3, 4)) + self.assertTrue(model.docvecs.vectors.shape == (2, 4)) + self.assertTrue(len(model.docvecs) == 2) + # check if inferring vectors for new documents and similarity search works. + doc0_inferred = model.infer_vector(list(DocsLeeCorpus())[0].words) + sims_to_infer = model.docvecs.most_similar([doc0_inferred], topn=len(model.docvecs)) + self.assertTrue(sims_to_infer) + # check if inferring vectors and similarity search works after saving and loading back the model + tmpf = get_tmpfile('gensim_doc2vec.tst') + model.save(tmpf) + loaded_model = doc2vec.Doc2Vec.load(tmpf) + doc0_inferred = loaded_model.infer_vector(list(DocsLeeCorpus())[0].words) + sims_to_infer = loaded_model.docvecs.most_similar([doc0_inferred], topn=len(loaded_model.docvecs)) + self.assertTrue(sims_to_infer) def testDoc2vecTrainParameters(self): diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index db24fdacd1..b522fe4131 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -864,9 +864,9 @@ def testPredictOutputWord(self): self.assertRaises(RuntimeError, model_without_neg.predict_output_word, ['system', 'human']) def testLoadOldModel(self): - """Test loading word2vec models from previous version""" + """Test loading an old word2vec model of indeterminate version""" - model_file = 'word2vec_old' + model_file = 'word2vec_old' # which version?!? model = word2vec.Word2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (12, 100)) self.assertTrue(len(model.wv.vocab) == 12) @@ -877,6 +877,9 @@ def testLoadOldModel(self): self.onlineSanity(model, trained_model=True) + def testLoadOldModelSeparates(self): + """Test loading an old word2vec model of indeterminate version""" + # Model stored in multiple files model_file = 'word2vec_old_sep' model = word2vec.Word2Vec.load(datapath(model_file)) @@ -889,41 +892,74 @@ def testLoadOldModel(self): self.onlineSanity(model, trained_model=True) + def test_load_old_models_pre_1_0(self): + """Test loading pre-1.0 models""" # load really old model model_file = 'w2v-lee-v0.12.0' model = word2vec.Word2Vec.load(datapath(model_file)) self.onlineSanity(model, trained_model=True) + old_versions = [ + '0.12.0', '0.12.1', '0.12.2', '0.12.3', '0.12.4', + '0.13.0', '0.13.1', '0.13.2', '0.13.3', '0.13.4', + ] + + for old_version in old_versions: + self._check_old_version(old_version) + + def test_load_old_models_1_x(self): + """Test loading 1.x models""" + + old_versions = [ + '1.0.0', '1.0.1', + ] + + for old_version in old_versions: + self._check_old_version(old_version) + + def test_load_old_models_2_x(self): + """Test loading 2.x models""" + + old_versions = [ + '2.0.0', '2.1.0', '2.2.0', '2.3.0', + ] + + for old_version in old_versions: + self._check_old_version(old_version) + + def test_load_old_models_3_x(self): + """Test loading 3.x models""" + # test for max_final_vocab for model saved in 3.3 model_file = 'word2vec_3.3' model = word2vec.Word2Vec.load(datapath(model_file)) self.assertEqual(model.max_final_vocab, None) self.assertEqual(model.vocabulary.max_final_vocab, None) - # Test loading word2vec models from all previous versions old_versions = [ - '0.12.0', '0.12.1', '0.12.2', '0.12.3', '0.12.4', - '0.13.0', '0.13.1', '0.13.2', '0.13.3', '0.13.4', - '1.0.0', '1.0.1', '2.0.0', '2.1.0', '2.2.0', '2.3.0', '3.0.0', '3.1.0', '3.2.0', '3.3.0', '3.4.0' ] - saved_models_dir = datapath('old_w2v_models/w2v_{}.mdl') for old_version in old_versions: - model = word2vec.Word2Vec.load(saved_models_dir.format(old_version)) - self.assertIsNone(model.corpus_total_words) - self.assertTrue(len(model.wv.vocab) == 3) - self.assertTrue(model.wv.vectors.shape == (3, 4)) - # check if similarity search and online training works. - self.assertTrue(len(model.wv.most_similar('sentence')) == 2) - model.build_vocab(list_corpus, update=True) - model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) - # check if similarity search and online training works after saving and loading back the model. - tmpf = get_tmpfile('gensim_word2vec.tst') - model.save(tmpf) - loaded_model = word2vec.Word2Vec.load(tmpf) - loaded_model.build_vocab(list_corpus, update=True) - loaded_model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) + self._check_old_version(old_version) + + def _check_old_version(self, old_version): + logging.info("TESTING LOAD of %s Word2Vec MODEL", old_version) + saved_models_dir = datapath('old_w2v_models/w2v_{}.mdl') + model = word2vec.Word2Vec.load(saved_models_dir.format(old_version)) + self.assertIsNone(model.corpus_total_words) + self.assertTrue(len(model.wv.vocab) == 3) + self.assertTrue(model.wv.vectors.shape == (3, 4)) + # check if similarity search and online training works. + self.assertTrue(len(model.wv.most_similar('sentence')) == 2) + model.build_vocab(list_corpus, update=True) + model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) + # check if similarity search and online training works after saving and loading back the model. + tmpf = get_tmpfile('gensim_word2vec.tst') + model.save(tmpf) + loaded_model = word2vec.Word2Vec.load(tmpf) + loaded_model.build_vocab(list_corpus, update=True) + loaded_model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) @log_capture() def testBuildVocabWarning(self, line): From 441269633a3ce71291cc95ffdc58a164d9aa9a92 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 17 Dec 2019 17:03:55 -0800 Subject: [PATCH 14/60] convert Vocab & related data items to use dataclasses --- gensim/models/doc2vec.py | 30 ++++++++++------ gensim/models/keyedvectors.py | 27 ++++++++++++-- gensim/models/word2vec.py | 57 ++++++++++++++++++++++++++---- gensim/models/wrappers/varembed.py | 5 ++- setup.py | 1 + 5 files changed, 98 insertions(+), 22 deletions(-) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 299dd82d31..ee619f0540 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -72,6 +72,7 @@ from collections import namedtuple, defaultdict from collections.abc import Iterable from timeit import default_timer +from dataclasses import dataclass from numpy import zeros, float32 as REAL, ones, \ memmap as np_memmap, vstack, integer, dtype @@ -145,21 +146,30 @@ def __str__(self): return '%s(%s, %s)' % (self.__class__.__name__, self.words, self.tags) -class Doctag(namedtuple('Doctag', 'index, word_count, doc_count')): - """A string document tag discovered during the initial vocabulary scan. - The document-vector equivalent of a Vocab object. TODO: merge with Vocab +@dataclass +class DoctagVocab: + """A dataclass shape-compatible with keyedvectors.SimpleVocab, extended to record + details of string document tags discovered during the initial vocabulary scan. Will not be used if all presented document tags are ints. """ - __slots__ = () - - def repeat(self, word_count): - return self._replace(word_count=self.word_count + word_count, doc_count=self.doc_count + 1) + __slots__ = ('doc_count', 'index', 'word_count') + doc_count: int # number of docs where tag appeared + index: int # position in underlying array + word_count: int # number of words in associated docs @property def count(self): return self.doc_count + @count.setter + def count(self, new_val): + self.doc_count = new_val + + +# compatibility alias, allowing prior namedtuples to unpickle +Doctag = DoctagVocab + class Doc2Vec(BaseWordEmbeddingsModel): def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, @@ -1030,7 +1040,8 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): max_rawint = max(max_rawint, tag) else: if tag in doctags_lookup: - doctags_lookup[tag] = doctags_lookup[tag].repeat(document_length) + doctags_lookup[tag].doc_count += 1 + doctags_lookup[tag].word_count += document_length else: doctags_lookup[tag] = Doctag(index=len(doctags_list), word_count=document_length, doc_count=1) doctags_list.append(tag) @@ -1046,8 +1057,7 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): if max_rawint > -1: # adjust indexes/list to account for range of pure-int keyed doctags for key in doctags_list: - orig = doctags_lookup[key] - doctags_lookup[key] = orig._replace(index=orig.index + max_rawint + 1) + doctags_lookup[key].index = doctags_lookup[key].index + max_rawint + 1 doctags_list = ConcatList([range(0, max_rawint + 1), doctags_list]) docvecs.vocab = doctags_lookup diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 723c99eb9a..841600d828 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -166,6 +166,7 @@ from itertools import chain import logging from collections import UserList +from dataclasses import dataclass from numbers import Integral try: @@ -347,7 +348,7 @@ def add(self, keys, weights, replace=False): # add new entities to the vocab for idx in np.nonzero(~in_vocab_mask)[0]: key = keys[idx] - self.map[key] = Vocab(index=len(self.index2key), count=1) + self.map[key] = SimpleVocab(index=len(self.index2key), count=1) self.index2key.append(key) # add vectors for new entities @@ -1407,11 +1408,27 @@ def _l2_norm(m, replace=False): return (m / dist).astype(REAL) -class Vocab(object): +@dataclass +class SimpleVocab: + """A single vocabulary item, used internally for collecting per-word position in the + backing array (.index), and frequency/sampling info from a corpus survey (.count). + + Using a dataclass with fixed __slots__ saves 200+ bytes per entry over the prior + approach (which used a freely-expandable __dict__) – but now requires specialized + uses to define their own expanded data items, which should always include `count` + and `index` properties. + """ + __slots__ = ('count', 'index') + count: int + index: int + + +class CompatVocab(object): def __init__(self, **kwargs): """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). + Retained for now to ease the loading of older models. """ self.count = 0 self.__dict__.update(kwargs) @@ -1424,6 +1441,10 @@ def __str__(self): return "%s(%s)" % (self.__class__.__name__, ', '.join(vals)) +# compatibility alias, allowing older pickle-based `.save()`s to load +Vocab = CompatVocab + + # Functions for internal use by _load_word2vec_format function def _add_word_to_result(result, counts, word, weights, vocab_size): @@ -1442,7 +1463,7 @@ def _add_word_to_result(result, counts, word, weights, vocab_size): logger.warning("vocabulary file is incomplete: '%s' is missing", word) word_count = None - result.vocab[word] = Vocab(index=word_id, count=word_count) + result.vocab[word] = SimpleVocab(index=word_id, count=word_count) result.vectors[word_id] = weights result.index2key.append(word) diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 88d41b09ff..b6a6c8c2d6 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -126,13 +126,15 @@ import heapq from timeit import default_timer from copy import deepcopy -from collections import defaultdict +from collections import defaultdict, namedtuple +from dataclasses import dataclass +from typing import List import threading import itertools import warnings from gensim.utils import keep_vocab_item, call_on_class_only, deprecated -from gensim.models.keyedvectors import Vocab, KeyedVectors, pseudorandom_weak_vector +from gensim.models.keyedvectors import KeyedVectors, pseudorandom_weak_vector from gensim.models.base_any2vec import BaseWordEmbeddingsModel try: @@ -1039,6 +1041,41 @@ def _scan_vocab_worker(stream, progress_queue, max_vocab_size=None, trim_rule=No return vocab +@dataclass +class W2VVocab: + """A dataclass shape-compatible with keyedvectors.SimpleVocab, extended with the + `sample_int` property needed by `Word2Vec` models.""" + __slots__ = ('count', 'index', 'sample_int') + count: int + index: int + sample_int: int + + def __init__(self, count=0, index=0, sample_int=2**32): + self.count, self.index, self.sample_int = count, index, sample_int + + def __lt__(self, other): + return self.count < other.count + + +@dataclass +class W2VHSVocab: + """A dataclass shape-compatible with W2VVocab, extended with the `code` and + `point` properties needed by hierarchical-sampling (`hs=1`) `Word2Vec` models.""" + __slots__ = ('count', 'index', 'sample_int', 'code', 'point') + count: int + index: int + sample_int: int + code: List[int] + point: List[int] + + def __init__(self, count=0, index=0, sample_int=2**32, code=None, point=None): + self.count, self.index, self.sample_int, self.code, self.point = \ + count, index, sample_int, code, point + + def __lt__(self, other): + return self.count < other.count + + class Word2VecVocab(utils.SaveLoad): def __init__( self, max_vocab_size=None, min_count=5, sample=1e-3, sorted_vocab=True, null_word=0, @@ -1161,7 +1198,7 @@ def prepare_vocab( retain_words.append(word) retain_total += v if not dry_run: - wv.vocab[word] = Vocab(count=v, index=len(wv.index2key)) + wv.vocab[word] = W2VVocab(count=v, index=len(wv.index2key)) wv.index2key.append(word) else: drop_unique += 1 @@ -1193,7 +1230,7 @@ def prepare_vocab( new_words.append(word) new_total += v if not dry_run: - wv.vocab[word] = Vocab(count=v, index=len(wv.index2key)) + wv.vocab[word] = W2VVocab(count=v, index=len(wv.index2key)) wv.index2key.append(word) else: drop_unique += 1 @@ -1267,7 +1304,7 @@ def prepare_vocab( return report_values def add_null_word(self, wv): - word, v = '\0', Vocab(count=1, sample_int=0) + word, v = '\0', W2VVocab(count=1, sample_int=0) v.index = len(wv.vocab) wv.index2key.append(word) wv.vocab[word] = v @@ -1305,13 +1342,18 @@ def make_cum_table(self, wv, domain=2**31 - 1): assert self.cum_table[-1] == domain +class Heapitem(namedtuple('Heapitem', 'count, index, left, right')): + def __lt__(self, other): + return self.count < other.count + + def _build_heap(vocab): heap = list(itervalues(vocab)) heapq.heapify(heap) for i in range(len(vocab) - 1): min1, min2 = heapq.heappop(heap), heapq.heappop(heap) heapq.heappush( - heap, Vocab(count=min1.count + min2.count, index=i + len(vocab), left=min1, right=min2) + heap, Heapitem(count=min1.count + min2.count, index=i + len(vocab), left=min1, right=min2) ) return heap @@ -1338,6 +1380,9 @@ def _assign_binary_codes(vocab): """ logger.info("constructing a huffman tree from %i words", len(vocab)) + for k in vocab.keys(): + # ensure dataclass items sufficient for huffman-encoding + vocab[k] = W2VHSVocab(vocab[k].count, vocab[k].index, vocab[k].sample_int) heap = _build_heap(vocab) if not heap: # diff --git a/gensim/models/wrappers/varembed.py b/gensim/models/wrappers/varembed.py index ca8227ac01..649d608fb3 100644 --- a/gensim/models/wrappers/varembed.py +++ b/gensim/models/wrappers/varembed.py @@ -18,8 +18,7 @@ import numpy as np from gensim import utils -from gensim.models.keyedvectors import KeyedVectors -from gensim.models.word2vec import Vocab +from gensim.models.keyedvectors import KeyedVectors, SimpleVocab logger = logging.getLogger(__name__) @@ -99,7 +98,7 @@ def load_word_embeddings(self, word_embeddings, word_to_ix): self.index2word = [None] * self.vocab_size logger.info("Corpus has %i words", len(self.vocab)) for word_id, word in enumerate(counts): - self.vocab[word] = Vocab(index=word_id, count=counts[word]) + self.vocab[word] = SimpleVocab(index=word_id, count=counts[word]) self.vectors[word_id] = word_embeddings[word_to_ix[word]] self.index2word[word_id] = word assert((len(self.vocab), self.vector_size) == self.vectors.shape) diff --git a/setup.py b/setup.py index 65b2324333..3f5423e813 100644 --- a/setup.py +++ b/setup.py @@ -333,6 +333,7 @@ def run(self): 'scipy >= 0.18.1', 'six >= 1.5.0', 'smart_open >= 1.8.1', + "dataclasses; python_version < '3.7'", ] setup_requires = [NUMPY_STR] From 65c2b2d3a749296cd0838ea8b017b95f857c3bc2 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 17 Dec 2019 18:33:26 -0800 Subject: [PATCH 15/60] rm obsolete Vocab/Trainable/abstract/Wrapper classes, persistent callbacks (bug #2136), outdated tests/warnings; update usages --- docs/src/apiref.rst | 7 - docs/src/models/base_any2vec.rst | 10 - docs/src/models/deprecated/doc2vec.rst | 9 - docs/src/models/deprecated/fasttext.rst | 10 - .../models/deprecated/fasttext_wrapper.rst | 10 - docs/src/models/deprecated/keyedvectors.rst | 9 - docs/src/models/deprecated/word2vec.rst | 9 - docs/src/models/wrappers/fasttext.rst | 9 - gensim/models/__init__.py | 1 - gensim/models/base_any2vec.py | 1251 ----------- gensim/models/callbacks.py | 14 +- gensim/models/deprecated/__init__.py | 1 - gensim/models/deprecated/doc2vec.py | 1044 --------- gensim/models/deprecated/fasttext.py | 711 ------ gensim/models/deprecated/fasttext_wrapper.py | 461 ---- gensim/models/deprecated/keyedvectors.py | 1115 ---------- gensim/models/deprecated/old_saveload.py | 398 ---- gensim/models/deprecated/word2vec.py | 1907 ---------------- gensim/models/doc2vec.py | 332 ++- gensim/models/doc2vec_inner.pyx | 20 +- gensim/models/fasttext.py | 325 +-- gensim/models/fasttext_inner.pyx | 14 +- gensim/models/keyedvectors.py | 9 +- gensim/models/word2vec.py | 1909 ++++++++++++----- gensim/models/word2vec_inner.pyx | 16 +- gensim/models/wrappers/__init__.py | 1 - gensim/models/wrappers/fasttext.py | 40 - gensim/sklearn_api/d2vmodel.py | 19 +- gensim/sklearn_api/ftmodel.py | 22 +- gensim/sklearn_api/w2vmodel.py | 22 +- gensim/test/test_doc2vec.py | 74 +- gensim/test/test_fasttext.py | 146 +- gensim/test/test_fasttext_wrapper.py | 382 ---- gensim/test/test_keras_integration.py | 4 +- gensim/test/test_keyedvectors.py | 6 +- gensim/test/test_poincare.py | 8 +- gensim/test/test_sklearn_api.py | 48 +- gensim/test/test_translation_matrix.py | 27 +- gensim/test/test_word2vec.py | 150 +- 39 files changed, 1902 insertions(+), 8648 deletions(-) delete mode 100644 docs/src/models/base_any2vec.rst delete mode 100644 docs/src/models/deprecated/doc2vec.rst delete mode 100644 docs/src/models/deprecated/fasttext.rst delete mode 100644 docs/src/models/deprecated/fasttext_wrapper.rst delete mode 100644 docs/src/models/deprecated/keyedvectors.rst delete mode 100644 docs/src/models/deprecated/word2vec.rst delete mode 100644 docs/src/models/wrappers/fasttext.rst delete mode 100644 gensim/models/base_any2vec.py delete mode 100644 gensim/models/deprecated/__init__.py delete mode 100644 gensim/models/deprecated/doc2vec.py delete mode 100644 gensim/models/deprecated/fasttext.py delete mode 100644 gensim/models/deprecated/fasttext_wrapper.py delete mode 100644 gensim/models/deprecated/keyedvectors.py delete mode 100644 gensim/models/deprecated/old_saveload.py delete mode 100644 gensim/models/deprecated/word2vec.py delete mode 100644 gensim/models/wrappers/fasttext.py delete mode 100644 gensim/test/test_fasttext_wrapper.py diff --git a/docs/src/apiref.rst b/docs/src/apiref.rst index e20c1e2f1f..1e3e341487 100644 --- a/docs/src/apiref.rst +++ b/docs/src/apiref.rst @@ -61,13 +61,6 @@ Modules: models/wrappers/ldavowpalwabbit.rst models/wrappers/wordrank models/wrappers/varembed - models/wrappers/fasttext - models/deprecated/doc2vec - models/deprecated/fasttext - models/deprecated/word2vec - models/deprecated/keyedvectors - models/deprecated/fasttext_wrapper - models/base_any2vec similarities/docsim similarities/termsim similarities/index diff --git a/docs/src/models/base_any2vec.rst b/docs/src/models/base_any2vec.rst deleted file mode 100644 index e6685cda66..0000000000 --- a/docs/src/models/base_any2vec.rst +++ /dev/null @@ -1,10 +0,0 @@ -:mod:`models.base_any2vec` -- Base classes for any2vec models -============================================================= - -.. automodule:: gensim.models.base_any2vec - :synopsis: Base classes for any2vec models - :members: - :inherited-members: - :special-members: __getitem__ - :undoc-members: - :show-inheritance: diff --git a/docs/src/models/deprecated/doc2vec.rst b/docs/src/models/deprecated/doc2vec.rst deleted file mode 100644 index e8fb2d96b3..0000000000 --- a/docs/src/models/deprecated/doc2vec.rst +++ /dev/null @@ -1,9 +0,0 @@ -:mod:`models.deprecated.doc2vec` -- Deep learning with paragraph2vec -==================================================================== - -.. automodule:: gensim.models.deprecated.doc2vec - :synopsis: Deep learning with doc2vec - :members: - :inherited-members: - :undoc-members: - :show-inheritance: diff --git a/docs/src/models/deprecated/fasttext.rst b/docs/src/models/deprecated/fasttext.rst deleted file mode 100644 index 08de0234d2..0000000000 --- a/docs/src/models/deprecated/fasttext.rst +++ /dev/null @@ -1,10 +0,0 @@ -:mod:`models.deprecated.fasttext` -- FastText model -=================================================== - -.. automodule:: gensim.models.deprecated.fasttext - :synopsis: FastText model - :members: - :inherited-members: - :special-members: __getitem__ - :undoc-members: - :show-inheritance: diff --git a/docs/src/models/deprecated/fasttext_wrapper.rst b/docs/src/models/deprecated/fasttext_wrapper.rst deleted file mode 100644 index 020504de24..0000000000 --- a/docs/src/models/deprecated/fasttext_wrapper.rst +++ /dev/null @@ -1,10 +0,0 @@ -:mod:`models.deprecated.fasttext_wrapper` -- Wrapper for Facebook implementation of FastText model -================================================================================================== - -.. automodule:: gensim.models.deprecated.fasttext_wrapper - :synopsis: FastText model - :members: - :inherited-members: - :special-members: __getitem__ - :undoc-members: - :show-inheritance: diff --git a/docs/src/models/deprecated/keyedvectors.rst b/docs/src/models/deprecated/keyedvectors.rst deleted file mode 100644 index 7d55cbc798..0000000000 --- a/docs/src/models/deprecated/keyedvectors.rst +++ /dev/null @@ -1,9 +0,0 @@ -:mod:`models.deprecated.keyedvectors` -- Store and query word vectors -===================================================================== - -.. automodule:: gensim.models.deprecated.keyedvectors - :synopsis: Store and query word vectors - :members: - :inherited-members: - :undoc-members: - :show-inheritance: diff --git a/docs/src/models/deprecated/word2vec.rst b/docs/src/models/deprecated/word2vec.rst deleted file mode 100644 index 3b80aaf196..0000000000 --- a/docs/src/models/deprecated/word2vec.rst +++ /dev/null @@ -1,9 +0,0 @@ -:mod:`models.deprecated.word2vec` -- Deep learning with word2vec -================================================================ - -.. automodule:: gensim.models.deprecated.word2vec - :synopsis: Deep learning with word2vec - :members: - :inherited-members: - :undoc-members: - :show-inheritance: diff --git a/docs/src/models/wrappers/fasttext.rst b/docs/src/models/wrappers/fasttext.rst deleted file mode 100644 index 4476cc7b43..0000000000 --- a/docs/src/models/wrappers/fasttext.rst +++ /dev/null @@ -1,9 +0,0 @@ -:mod:`models.wrappers.fasttext` -- Wrapper for FastText implementation from Facebook -==================================================================================== - -.. automodule:: gensim.models.wrappers.fasttext - :synopsis: FastText - :members: - :inherited-members: - :undoc-members: - :show-inheritance: diff --git a/gensim/models/__init__.py b/gensim/models/__init__.py index 96ca698b27..ee054b167d 100644 --- a/gensim/models/__init__.py +++ b/gensim/models/__init__.py @@ -23,7 +23,6 @@ from .translation_matrix import TranslationMatrix, BackMappingTranslationMatrix # noqa:F401 from . import wrappers # noqa:F401 -from . import deprecated # noqa:F401 from gensim import interfaces, utils diff --git a/gensim/models/base_any2vec.py b/gensim/models/base_any2vec.py deleted file mode 100644 index f0a33ba7ff..0000000000 --- a/gensim/models/base_any2vec.py +++ /dev/null @@ -1,1251 +0,0 @@ -#!/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 - -r"""This module contains base classes required for implementing \*2vec algorithms. - -The class hierarchy is designed to facilitate adding more concrete implementations for creating embeddings. -In the most general case, the purpose of this class is to transform an arbitrary representation to a numerical vector -(embedding). This is represented by the base :class:`~gensim.models.base_any2vec.BaseAny2VecModel`. The input space in -most cases (in the NLP field at least) is plain text. For this reason, we enrich the class hierarchy with the abstract -:class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` to be used as a base for models where the input -space is text. - -Notes ------ -Even though this is the usual case, not all embeddings transform text, such as the -:class:`~gensim.models.poincare.PoincareModel` that embeds graphs. - -See Also --------- -:class:`~gensim.models.word2vec.Word2Vec`. - Word2Vec model - embeddings for words. -:class:`~gensim.models.fasttext.FastText`. - FastText model - embeddings for words (ngram-based). -:class:`~gensim.models.doc2vec.Doc2Vec`. - Doc2Vec model - embeddings for documents. -:class:`~gensim.models.poincare.PoincareModel` - Poincare model - embeddings for graphs. - -""" - -from gensim import utils -import logging -from timeit import default_timer -import threading -from six.moves import range -from six import itervalues, string_types -from gensim import matutils -from numpy import float32 as REAL, ones, random, dtype -from types import GeneratorType -import os -import copy - - -try: - from queue import Queue -except ImportError: - from Queue import Queue - -logger = logging.getLogger(__name__) - - -class BaseAny2VecModel(utils.SaveLoad): - def __init__(self, workers=3, vector_size=100, epochs=5, callbacks=(), batch_words=10000): - r"""Base class for training, using and evaluating \*2vec model. - - Contains implementation for multi-threaded training. The purpose of this class is to provide a - reference interface for concrete embedding implementations, whether the input space is a corpus - of words, documents or anything else. At the same time, functionality that we expect to be common - for those implementations is provided here to avoid code duplication. - - In the special but usual case where the input space consists of words, a more specialized layer - is provided, consider inheriting from :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` - - Parameters - ---------- - workers : int, optional - Number of working threads, used for multithreading. - vector_size : int, optional - Dimensionality of the feature vectors. - epochs : int, optional - Number of iterations (epochs) of training through the corpus. - callbacks : list of :class:`~gensim.models.callbacks.CallbackAny2Vec`, optional - List of callbacks that need to be executed/run at specific stages during training. - batch_words : int, optional - Number of words to be processed by a single job. - - Notes - ----- - A subclass should initialize the following attributes: - - * self.kv - keyed vectors in model (see :class:`~gensim.models.keyedvectors.Word2VecKeyedVectors` as example) - * self.vocabulary - vocabulary (see :class:`~gensim.models.word2vec.Word2VecVocab` as example) - * self.trainables - internal matrices (see :class:`~gensim.models.word2vec.Word2VecTrainables` as example) - - """ - self.vector_size = int(vector_size) - self.workers = int(workers) - self.epochs = epochs - self.train_count = 0 - self.total_train_time = 0 - self.batch_words = batch_words - self.model_trimmed_post_training = False - self.callbacks = callbacks - - def _get_job_params(self, cur_epoch): - """Get job parameters required for each batch.""" - raise NotImplementedError() - - def _set_train_params(self, **kwargs): - """Set model parameters required for training.""" - raise NotImplementedError() - - def _update_job_params(self, job_params, epoch_progress, cur_epoch): - """Get updated job parameters based on the epoch_progress and cur_epoch.""" - raise NotImplementedError() - - def _get_thread_working_mem(self): - """Get private working memory per thread.""" - raise NotImplementedError() - - def _raw_word_count(self, job): - """Get the number of words in a given job.""" - raise NotImplementedError() - - def _clear_post_train(self): - """Resets certain properties of the model post training. eg. `keyedvectors.vectors_norm`.""" - raise NotImplementedError() - - def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, - total_examples=None, total_words=None, **kwargs): - raise NotImplementedError() - - def _do_train_job(self, data_iterable, job_parameters, thread_private_mem): - """Train a single batch. Return 2-tuple `(effective word count, total word count)`.""" - raise NotImplementedError() - - def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs): - """Check that the training parameters provided make sense. e.g. raise error if `epochs` not provided.""" - raise NotImplementedError() - - def _check_input_data_sanity(self, data_iterable=None, corpus_file=None): - """Check that only one argument is None.""" - if not (data_iterable is None) ^ (corpus_file is None): - raise ValueError("You must provide only one of singlestream or corpus_file arguments.") - - def _worker_loop_corpusfile(self, corpus_file, thread_id, offset, cython_vocab, progress_queue, cur_epoch=0, - total_examples=None, total_words=None, **kwargs): - """Train the model on a `corpus_file` in LineSentence format. - - This function will be called in parallel by multiple workers (threads or processes) to make - optimal use of multicore machines. - - Parameters - ---------- - corpus_file : str - Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. - thread_id : int - Thread index starting from 0 to `number of workers - 1`. - offset : int - Offset (in bytes) in the `corpus_file` for particular worker. - cython_vocab : :class:`~gensim.models.word2vec_inner.CythonVocab` - Copy of the vocabulary in order to access it without GIL. - progress_queue : Queue of (int, int, int) - A queue of progress reports. Each report is represented as a tuple of these 3 elements: - * Size of data chunk processed, for example number of sentences in the corpus chunk. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - * Total word count used in training. - **kwargs : object - Additional key word parameters for the specific model inheriting from this class. - - """ - thread_private_mem = self._get_thread_working_mem() - - examples, tally, raw_tally = self._do_train_epoch( - corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, - total_examples=total_examples, total_words=total_words, **kwargs) - - progress_queue.put((examples, tally, raw_tally)) - progress_queue.put(None) - - def _worker_loop(self, job_queue, progress_queue): - """Train the model, lifting batches of data from the queue. - - This function will be called in parallel by multiple workers (threads or processes) to make - optimal use of multicore machines. - - Parameters - ---------- - job_queue : Queue of (list of objects, (str, int)) - A queue of jobs still to be processed. The worker will take up jobs from this queue. - Each job is represented by a tuple where the first element is the corpus chunk to be processed and - the second is the dictionary of parameters. - progress_queue : Queue of (int, int, int) - A queue of progress reports. Each report is represented as a tuple of these 3 elements: - * Size of data chunk processed, for example number of sentences in the corpus chunk. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - * Total word count used in training. - - """ - thread_private_mem = self._get_thread_working_mem() - jobs_processed = 0 - while True: - job = job_queue.get() - if job is None: - progress_queue.put(None) - break # no more jobs => quit this worker - data_iterable, job_parameters = job - - for callback in self.callbacks: - callback.on_batch_begin(self) - - tally, raw_tally = self._do_train_job(data_iterable, job_parameters, thread_private_mem) - - for callback in self.callbacks: - callback.on_batch_end(self) - - progress_queue.put((len(data_iterable), tally, raw_tally)) # report back progress - jobs_processed += 1 - logger.debug("worker exiting, processed %i jobs", jobs_processed) - - def _job_producer(self, data_iterator, job_queue, cur_epoch=0, total_examples=None, total_words=None): - """Fill the jobs queue using the data found in the input stream. - - Each job is represented by a tuple where the first element is the corpus chunk to be processed and - the second is a dictionary of parameters. - - Parameters - ---------- - data_iterator : iterable of list of objects - The input dataset. This will be split in chunks and these chunks will be pushed to the queue. - job_queue : Queue of (list of object, dict of (str, int)) - A queue of jobs still to be processed. The worker will take up jobs from this queue. - Each job is represented by a tuple where the first element is the corpus chunk to be processed and - the second is the dictionary of parameters. - cur_epoch : int, optional - The current training epoch, needed to compute the training parameters for each job. - For example in many implementations the learning rate would be dropping with the number of epochs. - total_examples : int, optional - Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences - in a corpus. Used to log progress. - total_words : int, optional - Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw - words in a corpus. Used to log progress. - - """ - job_batch, batch_size = [], 0 - pushed_words, pushed_examples = 0, 0 - next_job_params = self._get_job_params(cur_epoch) - job_no = 0 - - for data_idx, data in enumerate(data_iterator): - data_length = self._raw_word_count([data]) - - # can we fit this sentence into the existing job batch? - if batch_size + data_length <= self.batch_words: - # yes => add it to the current job - job_batch.append(data) - batch_size += data_length - else: - job_no += 1 - job_queue.put((job_batch, next_job_params)) - - # update the learning rate for the next job - if total_examples: - # examples-based decay - pushed_examples += len(job_batch) - epoch_progress = 1.0 * pushed_examples / total_examples - else: - # words-based decay - pushed_words += self._raw_word_count(job_batch) - epoch_progress = 1.0 * pushed_words / total_words - next_job_params = self._update_job_params(next_job_params, epoch_progress, cur_epoch) - - # add the sentence that didn't fit as the first item of a new job - job_batch, batch_size = [data], data_length - # add the last job too (may be significantly smaller than batch_words) - if job_batch: - job_no += 1 - job_queue.put((job_batch, next_job_params)) - - if job_no == 0 and self.train_count == 0: - logger.warning( - "train() called with an empty iterator (if not intended, " - "be sure to provide a corpus that offers restartable iteration = an iterable)." - ) - - # give the workers heads up that they can finish -- no more work! - for _ in range(self.workers): - job_queue.put(None) - logger.debug("job loop exiting, total %i jobs", job_no) - - def _log_progress(self, job_queue, progress_queue, cur_epoch, example_count, total_examples, - raw_word_count, total_words, trained_word_count, elapsed): - raise NotImplementedError() - - def _log_epoch_end(self, cur_epoch, example_count, total_examples, raw_word_count, total_words, - trained_word_count, elapsed, is_corpus_file_mode): - raise NotImplementedError() - - def _log_train_end(self, raw_word_count, trained_word_count, total_elapsed, job_tally): - raise NotImplementedError() - - def _log_epoch_progress(self, progress_queue=None, job_queue=None, cur_epoch=0, total_examples=None, - total_words=None, report_delay=1.0, is_corpus_file_mode=None): - """Get the progress report for a single training epoch. - - Parameters - ---------- - progress_queue : Queue of (int, int, int) - A queue of progress reports. Each report is represented as a tuple of these 3 elements: - * size of data chunk processed, for example number of sentences in the corpus chunk. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - * Total word count used in training. - job_queue : Queue of (list of object, dict of (str, int)) - A queue of jobs still to be processed. The worker will take up jobs from this queue. - Each job is represented by a tuple where the first element is the corpus chunk to be processed and - the second is the dictionary of parameters. - cur_epoch : int, optional - The current training epoch, needed to compute the training parameters for each job. - For example in many implementations the learning rate would be dropping with the number of epochs. - total_examples : int, optional - Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences - in a corpus. Used to log progress. - total_words : int, optional - Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw - words in a corpus. Used to log progress. - report_delay : float, optional - Number of seconds between two consecutive progress report messages in the logger. - is_corpus_file_mode : bool, optional - Whether training is file-based (corpus_file argument) or not. - - Returns - ------- - (int, int, int) - The epoch report consisting of three elements: - * size of data chunk processed, for example number of sentences in the corpus chunk. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - * Total word count used in training. - - """ - example_count, trained_word_count, raw_word_count = 0, 0, 0 - start, next_report = default_timer() - 0.00001, 1.0 - job_tally = 0 - unfinished_worker_count = self.workers - - while unfinished_worker_count > 0: - report = progress_queue.get() # blocks if workers too slow - if report is None: # a thread reporting that it finished - unfinished_worker_count -= 1 - logger.info("worker thread finished; awaiting finish of %i more threads", unfinished_worker_count) - continue - examples, trained_words, raw_words = report - job_tally += 1 - - # update progress stats - example_count += examples - trained_word_count += trained_words # only words in vocab & sampled - raw_word_count += raw_words - - # log progress once every report_delay seconds - elapsed = default_timer() - start - if elapsed >= next_report: - self._log_progress( - job_queue, progress_queue, cur_epoch, example_count, total_examples, - raw_word_count, total_words, trained_word_count, elapsed) - next_report = elapsed + report_delay - # all done; report the final stats - elapsed = default_timer() - start - self._log_epoch_end( - cur_epoch, example_count, total_examples, raw_word_count, total_words, - trained_word_count, elapsed, is_corpus_file_mode) - self.total_train_time += elapsed - return trained_word_count, raw_word_count, job_tally - - def _train_epoch_corpusfile(self, corpus_file, cur_epoch=0, total_examples=None, total_words=None, **kwargs): - """Train the model for a single epoch. - - Parameters - ---------- - corpus_file : str - Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. - cur_epoch : int, optional - The current training epoch, needed to compute the training parameters for each job. - For example in many implementations the learning rate would be dropping with the number of epochs. - total_examples : int, optional - Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences - in a corpus, used to log progress. - total_words : int - Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw - words in a corpus, used to log progress. Must be provided in order to seek in `corpus_file`. - **kwargs : object - Additional key word parameters for the specific model inheriting from this class. - - Returns - ------- - (int, int, int) - The training report for this epoch consisting of three elements: - * Size of data chunk processed, for example number of sentences in the corpus chunk. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - * Total word count used in training. - - """ - if not total_words: - raise ValueError("total_words must be provided alongside corpus_file argument.") - - from gensim.models.word2vec_corpusfile import CythonVocab - from gensim.models.fasttext import FastText - cython_vocab = CythonVocab(self.wv, hs=self.hs, fasttext=isinstance(self, FastText)) - - progress_queue = Queue() - - corpus_file_size = os.path.getsize(corpus_file) - - thread_kwargs = copy.copy(kwargs) - thread_kwargs['cur_epoch'] = cur_epoch - thread_kwargs['total_examples'] = total_examples - thread_kwargs['total_words'] = total_words - workers = [ - threading.Thread( - target=self._worker_loop_corpusfile, - args=( - corpus_file, thread_id, corpus_file_size / self.workers * thread_id, cython_vocab, progress_queue - ), - kwargs=thread_kwargs - ) for thread_id in range(self.workers) - ] - - for thread in workers: - thread.daemon = True - thread.start() - - trained_word_count, raw_word_count, job_tally = self._log_epoch_progress( - progress_queue=progress_queue, job_queue=None, cur_epoch=cur_epoch, - total_examples=total_examples, total_words=total_words, is_corpus_file_mode=True) - - return trained_word_count, raw_word_count, job_tally - - def _train_epoch(self, data_iterable, cur_epoch=0, total_examples=None, total_words=None, - queue_factor=2, report_delay=1.0): - """Train the model for a single epoch. - - Parameters - ---------- - data_iterable : iterable of list of object - The input corpus. This will be split in chunks and these chunks will be pushed to the queue. - cur_epoch : int, optional - The current training epoch, needed to compute the training parameters for each job. - For example in many implementations the learning rate would be dropping with the number of epochs. - total_examples : int, optional - Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences - in a corpus, used to log progress. - total_words : int, optional - Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw - words in a corpus, used to log progress. - queue_factor : int, optional - Multiplier for size of queue -> size = number of workers * queue_factor. - report_delay : float, optional - Number of seconds between two consecutive progress report messages in the logger. - - Returns - ------- - (int, int, int) - The training report for this epoch consisting of three elements: - * Size of data chunk processed, for example number of sentences in the corpus chunk. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - * Total word count used in training. - - """ - job_queue = Queue(maxsize=queue_factor * self.workers) - progress_queue = Queue(maxsize=(queue_factor + 1) * self.workers) - - workers = [ - threading.Thread( - target=self._worker_loop, - args=(job_queue, progress_queue,)) - for _ in range(self.workers) - ] - - workers.append(threading.Thread( - target=self._job_producer, - args=(data_iterable, job_queue), - kwargs={'cur_epoch': cur_epoch, 'total_examples': total_examples, 'total_words': total_words})) - - for thread in workers: - thread.daemon = True # make interrupting the process with ctrl+c easier - thread.start() - - trained_word_count, raw_word_count, job_tally = self._log_epoch_progress( - progress_queue, job_queue, cur_epoch=cur_epoch, total_examples=total_examples, total_words=total_words, - report_delay=report_delay, is_corpus_file_mode=False) - - return trained_word_count, raw_word_count, job_tally - - def train(self, data_iterable=None, corpus_file=None, epochs=None, total_examples=None, - total_words=None, queue_factor=2, report_delay=1.0, callbacks=(), **kwargs): - """Train the model for multiple epochs using multiple workers. - - Parameters - ---------- - data_iterable : iterable of list of object - The input corpus. This will be split in chunks and these chunks will be pushed to the queue. - corpus_file : str, optional - Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. - If you use this argument instead of `data_iterable`, you must provide `total_words` argument as well. - epochs : int, optional - Number of epochs (training iterations over the whole input) of training. - total_examples : int, optional - Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences - in a corpus, used to log progress. - total_words : int, optional - Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw - words in a corpus, used to log progress. - queue_factor : int, optional - Multiplier for size of queue -> size = number of workers * queue_factor. - report_delay : float, optional - Number of seconds between two consecutive progress report messages in the logger. - callbacks : list of :class:`~gensim.models.callbacks.CallbackAny2Vec`, optional - List of callbacks to execute at specific stages during training. - **kwargs : object - Additional key word parameters for the specific model inheriting from this class. - - Returns - ------- - (int, int) - The total training report consisting of two elements: - * size of total data processed, for example number of sentences in the whole corpus. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - - """ - self._set_train_params(**kwargs) - if callbacks: - self.callbacks = callbacks - self.epochs = epochs - self._check_training_sanity( - epochs=epochs, - total_examples=total_examples, - total_words=total_words, **kwargs) - - for callback in self.callbacks: - callback.on_train_begin(self) - - trained_word_count = 0 - raw_word_count = 0 - start = default_timer() - 0.00001 - job_tally = 0 - - for cur_epoch in range(self.epochs): - for callback in self.callbacks: - callback.on_epoch_begin(self) - - if data_iterable is not None: - trained_word_count_epoch, raw_word_count_epoch, job_tally_epoch = self._train_epoch( - data_iterable, cur_epoch=cur_epoch, total_examples=total_examples, - total_words=total_words, queue_factor=queue_factor, report_delay=report_delay) - else: - trained_word_count_epoch, raw_word_count_epoch, job_tally_epoch = self._train_epoch_corpusfile( - corpus_file, cur_epoch=cur_epoch, total_examples=total_examples, total_words=total_words, **kwargs) - - trained_word_count += trained_word_count_epoch - raw_word_count += raw_word_count_epoch - job_tally += job_tally_epoch - - for callback in self.callbacks: - callback.on_epoch_end(self) - - # Log overall time - total_elapsed = default_timer() - start - self._log_train_end(raw_word_count, trained_word_count, total_elapsed, job_tally) - - self.train_count += 1 # number of times train() has been called - self._clear_post_train() - - for callback in self.callbacks: - callback.on_train_end(self) - return trained_word_count, raw_word_count - - @classmethod - def load(cls, fname_or_handle, **kwargs): - """Load a previously saved object (using :meth:`gensim.models.base_any2vec.BaseAny2VecModel.save`) from a file. - - Parameters - ---------- - fname_or_handle : {str, file-like object} - Path to file that contains needed object or handle to an open file. - **kwargs : object - Keyword arguments propagated to :meth:`~gensim.utils.SaveLoad.load`. - - See Also - -------- - :meth:`~gensim.models.base_any2vec.BaseAny2VecModel.save` - Method for save a model. - - Returns - ------- - object - Object loaded from `fname_or_handle`. - - Raises - ------ - IOError - When methods are called on an instance (should be called on a class, this is a class method). - - """ - return super(BaseAny2VecModel, cls).load(fname_or_handle, **kwargs) - - def save(self, fname_or_handle, **kwargs): - """Save the object to file. - - Parameters - ---------- - fname_or_handle : {str, file-like object} - Path to file where the model will be persisted. - **kwargs : object - Key word arguments propagated to :meth:`~gensim.utils.SaveLoad.save`. - - See Also - -------- - :meth:`~gensim.models.base_any2vec.BaseAny2VecModel.load` - Method for load model after current method. - - """ - super(BaseAny2VecModel, self).save(fname_or_handle, **kwargs) - - -class BaseWordEmbeddingsModel(BaseAny2VecModel): - def __init__(self, sentences=None, corpus_file=None, workers=3, vector_size=100, epochs=5, callbacks=(), - batch_words=10000, trim_rule=None, sg=0, alpha=0.025, window=5, seed=1, hs=0, negative=5, - ns_exponent=0.75, cbow_mean=1, min_alpha=0.0001, compute_loss=False, **kwargs): - """Base class containing common methods for training, using & evaluating word embeddings learning models. - - Parameters - ---------- - sentences : iterable of list of str, optional - Can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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. - corpus_file : str, optional - Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. - You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or - `corpus_file` arguments need to be passed (or none of them, in that case, the model is left uninitialized). - workers : int, optional - Number of working threads, used for multiprocessing. - vector_size : int, optional - Dimensionality of the feature vectors. - epochs : int, optional - Number of iterations (epochs) of training through the corpus. - callbacks : list of :class:`~gensim.models.callbacks.CallbackAny2Vec`, optional - List of callbacks that need to be executed/run at specific stages during training. - batch_words : int, optional - Number of words to be processed by a single job. - trim_rule : function, optional - 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, look to :func:`~gensim.utils.keep_vocab_item`), - or a callable that accepts parameters (word, count, min_count) and returns either - :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`. - The rule, if given, is only used to prune vocabulary during current method call and is not stored as part - of the model. - - The input parameters are of the following types: - * `word` (str) - the word we are examining - * `count` (int) - the word's frequency count in the corpus - * `min_count` (int) - the minimum count threshold. - - sg : {1, 0}, optional - Defines the training algorithm. If 1, skip-gram is used, otherwise, CBOW is employed. - alpha : float, optional - The beginning learning rate. This will linearly reduce with iterations until it reaches `min_alpha`. - window : int, optional - The maximum distance between the current and predicted word within a sentence. - seed : int, optional - Seed for the random number generator. Initial vectors for each word are seeded with a hash of - the concatenation of word + `str(seed)`. - Note that for a fully deterministically-reproducible run, you must also limit the model to a single worker - thread (`workers=1`), 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. - hs : {1,0}, optional - If 1, hierarchical softmax will be used for model training. - If set to 0, and `negative` is non-zero, negative sampling will be used. - negative : int, optional - If > 0, negative sampling will be used, the int for negative specifies how many "noise words" - should be drawn (usually between 5-20). - If set to 0, no negative sampling is used. - cbow_mean : {1,0}, optional - If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used. - min_alpha : float, optional - Final learning rate. Drops linearly with the number of iterations from `alpha`. - compute_loss : bool, optional - If True, loss will be computed while training the Word2Vec model and stored in - :attr:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.running_training_loss` attribute. - **kwargs : object - Key word arguments needed to allow children classes to accept more arguments. - - See Also - -------- - :class:`~gensim.models.word2vec.Word2Vec`. - Word2Vec model - embeddings for words. - :class:`~gensim.models.fasttext.FastText`. - FastText model - embeddings for words (ngram-based). - :class:`~gensim.models.doc2vec.Doc2Vec`. - Doc2Vec model - embeddings for documents. - :class:`~gensim.models.poincare.PoincareModel` - Poincare model - embeddings for graphs. - - """ - self.sg = int(sg) - if vector_size % 4 != 0: - logger.warning("consider setting layer size to a multiple of 4 for greater performance") - self.alpha = float(alpha) - self.window = int(window) - self.random = random.RandomState(seed) - self.min_alpha = float(min_alpha) - self.hs = int(hs) - self.negative = int(negative) - self.ns_exponent = ns_exponent - self.cbow_mean = int(cbow_mean) - self.compute_loss = bool(compute_loss) - self.running_training_loss = 0 - self.min_alpha_yet_reached = float(alpha) - self.corpus_count = 0 - self.corpus_total_words = 0 - - super(BaseWordEmbeddingsModel, self).__init__( - workers=workers, vector_size=vector_size, epochs=epochs, callbacks=callbacks, batch_words=batch_words) - - if sentences is not None or corpus_file is not None: - self._check_input_data_sanity(data_iterable=sentences, corpus_file=corpus_file) - if corpus_file is not None and not isinstance(corpus_file, string_types): - raise TypeError("You must pass string as the corpus_file argument.") - elif isinstance(sentences, GeneratorType): - raise TypeError("You can't pass a generator as the sentences argument. Try a sequence.") - - self.build_vocab(sentences=sentences, corpus_file=corpus_file, trim_rule=trim_rule) - self.train( - sentences=sentences, corpus_file=corpus_file, total_examples=self.corpus_count, - total_words=self.corpus_total_words, epochs=self.epochs, start_alpha=self.alpha, - end_alpha=self.min_alpha, compute_loss=compute_loss) - else: - if trim_rule is not None: - logger.warning( - "The rule, if given, is only used to prune vocabulary during build_vocab() " - "and is not stored as part of the model. Model initialized without sentences. " - "trim_rule provided, if any, will be ignored.") - - def _clear_post_train(self): - raise NotImplementedError() - - def _do_train_job(self, data_iterable, job_parameters, thread_private_mem): - raise NotImplementedError() - - def _set_train_params(self, **kwargs): - raise NotImplementedError() - - def __str__(self): - """Get a human readable representation of the object. - - Returns - ------- - str - A human readable string containing the class name, as well as the size of dictionary, number of - features and starting learning rate used by the object. - - """ - return "%s(vocab=%s, size=%s, alpha=%s)" % ( - self.__class__.__name__, len(self.wv.index2word), self.vector_size, self.alpha - ) - - def build_vocab(self, sentences=None, corpus_file=None, update=False, progress_per=10000, - keep_raw_vocab=False, trim_rule=None, **kwargs): - """Build vocabulary from a sequence of sentences (can be a once-only generator stream). - - Parameters - ---------- - sentences : iterable of list of str - Can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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` module for such examples. - corpus_file : str, optional - Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. - You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or - `corpus_file` arguments need to be passed (not both of them). - update : bool - If true, the new words in `sentences` will be added to model's vocab. - progress_per : int, optional - Indicates how many words to process before showing/updating the progress. - keep_raw_vocab : bool, optional - If False, the raw vocabulary will be deleted after the scaling is done to free up RAM. - trim_rule : function, optional - 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, look to :func:`~gensim.utils.keep_vocab_item`), - or a callable that accepts parameters (word, count, min_count) and returns either - :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`. - The rule, if given, is only used to prune vocabulary during current method call and is not stored as part - of the model. - - The input parameters are of the following types: - * `word` (str) - the word we are examining - * `count` (int) - the word's frequency count in the corpus - * `min_count` (int) - the minimum count threshold. - - **kwargs : object - Key word arguments propagated to `self.vocabulary.prepare_vocab` - - """ - total_words, corpus_count = self.vocabulary.scan_vocab( - sentences=sentences, corpus_file=corpus_file, progress_per=progress_per, trim_rule=trim_rule) - self.corpus_count = corpus_count - self.corpus_total_words = total_words - report_values = self.vocabulary.prepare_vocab( - self.hs, self.negative, self.wv, update=update, keep_raw_vocab=keep_raw_vocab, - trim_rule=trim_rule, **kwargs) - report_values['memory'] = self.estimate_memory(vocab_size=report_values['num_retained_words']) - self.trainables.prepare_weights(self.hs, self.negative, self.wv, update=update, vocabulary=self.vocabulary) - - def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=None, trim_rule=None, update=False): - """Build vocabulary from a dictionary of word frequencies. - - Parameters - ---------- - word_freq : dict of (str, int) - A mapping from a word in the vocabulary to its frequency count. - keep_raw_vocab : bool, optional - If False, delete the raw vocabulary after the scaling is done to free up RAM. - corpus_count : int, optional - Even if no corpus is provided, this argument can set corpus_count explicitly. - trim_rule : function, optional - 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, look to :func:`~gensim.utils.keep_vocab_item`), - or a callable that accepts parameters (word, count, min_count) and returns either - :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`. - The rule, if given, is only used to prune vocabulary during current method call and is not stored as part - of the model. - - The input parameters are of the following types: - * `word` (str) - the word we are examining - * `count` (int) - the word's frequency count in the corpus - * `min_count` (int) - the minimum count threshold. - - update : bool, optional - If true, the new provided words in `word_freq` dict will be added to model's vocab. - - """ - logger.info("Processing provided word frequencies") - # Instead of scanning text, this will assign provided word frequencies dictionary(word_freq) - # to be directly the raw vocab - raw_vocab = word_freq - logger.info( - "collected %i different raw word, with total frequency of %i", - len(raw_vocab), sum(itervalues(raw_vocab)) - ) - - # Since no sentences are provided, this is to control the corpus_count. - self.corpus_count = corpus_count or 0 - self.vocabulary.raw_vocab = raw_vocab - - # trim by min_count & precalculate downsampling - report_values = self.vocabulary.prepare_vocab( - self.hs, self.negative, self.wv, keep_raw_vocab=keep_raw_vocab, - trim_rule=trim_rule, update=update) - report_values['memory'] = self.estimate_memory(vocab_size=report_values['num_retained_words']) - self.trainables.prepare_weights( - self.hs, self.negative, self.wv, update=update, vocabulary=self.vocabulary) # build tables & arrays - - def estimate_memory(self, vocab_size=None, report=None): - """Estimate required memory for a model using current settings and provided vocabulary size. - - Parameters - ---------- - vocab_size : int, optional - Number of unique tokens in the vocabulary - report : dict of (str, int), optional - A dictionary from string representations of the model's memory consuming members to their size in bytes. - - Returns - ------- - dict of (str, int) - A dictionary from string representations of the model's memory consuming members to their size in bytes. - - """ - vocab_size = vocab_size or len(self.wv.vocab) - report = report or {} - report['vocab'] = vocab_size * (700 if self.hs else 500) - report['vectors'] = vocab_size * self.vector_size * dtype(REAL).itemsize - if self.hs: - report['syn1'] = vocab_size * self.trainables.layer1_size * dtype(REAL).itemsize - if self.negative: - report['syn1neg'] = vocab_size * self.trainables.layer1_size * dtype(REAL).itemsize - report['total'] = sum(report.values()) - logger.info( - "estimated required memory for %i words and %i dimensions: %i bytes", - vocab_size, self.vector_size, report['total'] - ) - return report - - def train(self, sentences=None, corpus_file=None, total_examples=None, total_words=None, - epochs=None, start_alpha=None, end_alpha=None, word_count=0, - queue_factor=2, report_delay=1.0, compute_loss=False, callbacks=(), **kwargs): - """Train the model. If the hyper-parameters are passed, they override the ones set in the constructor. - - Parameters - ---------- - sentences : iterable of list of str - Can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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` module for such examples. - corpus_file : str, optional - Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. - You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or - `corpus_file` arguments need to be passed (not both of them). - total_examples : int, optional - Count of sentences. - total_words : int, optional - Count of raw words in sentences. - epochs : int, optional - Number of iterations (epochs) over the corpus. - start_alpha : float, optional - Initial learning rate. - end_alpha : float, optional - Final learning rate. Drops linearly with the number of iterations from `start_alpha`. - word_count : int, optional - Count of words already trained. Leave this to 0 for the usual case of training on all words in sentences. - queue_factor : int, optional - Multiplier for size of queue -> size = number of workers * queue_factor. - report_delay : float, optional - Seconds to wait before reporting progress. - compute_loss : bool, optional - If True, loss will be computed while training the Word2Vec model and stored in - :attr:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.running_training_loss`. - callbacks : list of :class:`~gensim.models.callbacks.CallbackAny2Vec`, optional - List of callbacks that need to be executed/run at specific stages during training. - **kwargs : object - Additional key word parameters for the specific model inheriting from this class. - - Returns - ------- - (int, int) - Tuple of (effective word count after ignoring unknown words and sentence length trimming, total word count). - - """ - - self.alpha = start_alpha or self.alpha - self.min_alpha = end_alpha or self.min_alpha - self.compute_loss = compute_loss - self.running_training_loss = 0.0 - return super(BaseWordEmbeddingsModel, self).train( - data_iterable=sentences, corpus_file=corpus_file, total_examples=total_examples, - total_words=total_words, epochs=epochs, start_alpha=start_alpha, end_alpha=end_alpha, word_count=word_count, - queue_factor=queue_factor, report_delay=report_delay, compute_loss=compute_loss, callbacks=callbacks, - **kwargs) - - def _get_job_params(self, cur_epoch): - """Get the learning rate used in the current epoch. - - Parameters - ---------- - cur_epoch : int - Current iteration through the corpus - - Returns - ------- - float - The learning rate for this epoch (it is linearly reduced with epochs from `self.alpha` to `self.min_alpha`). - - """ - alpha = self.alpha - ((self.alpha - self.min_alpha) * float(cur_epoch) / self.epochs) - return alpha - - def _update_job_params(self, job_params, epoch_progress, cur_epoch): - """Get the correct learning rate for the next iteration. - - Parameters - ---------- - job_params : dict of (str, obj) - UNUSED. - epoch_progress : float - Ratio of finished work in the current epoch. - cur_epoch : int - Number of current iteration. - - Returns - ------- - float - The learning rate to be used in the next training epoch. - - """ - start_alpha = self.alpha - end_alpha = self.min_alpha - progress = (cur_epoch + epoch_progress) / self.epochs - next_alpha = start_alpha - (start_alpha - end_alpha) * progress - next_alpha = max(end_alpha, next_alpha) - self.min_alpha_yet_reached = next_alpha - return next_alpha - - def _get_thread_working_mem(self): - """Computes the memory used per worker thread. - - Returns - ------- - (np.ndarray, np.ndarray) - Each worker threads private work memory. - - """ - work = matutils.zeros_aligned(self.trainables.layer1_size, dtype=REAL) # per-thread private work memory - neu1 = matutils.zeros_aligned(self.trainables.layer1_size, dtype=REAL) - return work, neu1 - - def _raw_word_count(self, job): - """Get the number of words in a given job. - - Parameters - ---------- - job: iterable of list of str - The corpus chunk processed in a single batch. - - Returns - ------- - int - Number of raw words in the corpus chunk. - - """ - return sum(len(sentence) for sentence in job) - - def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs): - """Checks whether the training parameters make sense. - - Called right before training starts in :meth:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.train` - and raises warning or errors depending on the severity of the issue in case an inconsistent parameter - combination is detected. - - Parameters - ---------- - epochs : int, optional - Number of training epochs. Must have a (non None) value. - total_examples : int, optional - Number of documents in the corpus. Either `total_examples` or `total_words` **must** be supplied. - total_words : int, optional - Number of words in the corpus. Either `total_examples` or `total_words` **must** be supplied. - **kwargs : object - Unused. Present to preserve signature among base and inherited implementations. - - Raises - ------ - RuntimeError - If one of the required training pre/post processing steps have not been performed. - ValueError - If the combination of input parameters is inconsistent. - - """ - if self.alpha > self.min_alpha_yet_reached: - logger.warning("Effective 'alpha' higher than previous training cycles") - if self.model_trimmed_post_training: - raise RuntimeError("Parameters for training were discarded using model_trimmed_post_training method") - - if not self.wv.vocab: # should be set by `build_vocab` - raise RuntimeError("you must first build vocabulary before training the model") - if not len(self.wv.vectors): - raise RuntimeError("you must initialize vectors before training the model") - - if not hasattr(self, 'corpus_count'): - raise ValueError( - "The number of examples in the training corpus is missing. " - "Please make sure this is set inside `build_vocab` function." - "Call the `build_vocab` function before calling `train`." - ) - - if total_words is None and total_examples is None: - raise ValueError( - "You must specify either total_examples or total_words, for proper job parameters updation" - "and progress calculations. " - "The usual value is total_examples=model.corpus_count." - ) - if epochs is None: - raise ValueError("You must specify an explict epochs count. The usual value is epochs=model.epochs.") - logger.info( - "training model with %i workers on %i vocabulary and %i features, " - "using sg=%s hs=%s sample=%s negative=%s window=%s", - self.workers, len(self.wv.vocab), self.trainables.layer1_size, self.sg, - self.hs, self.vocabulary.sample, self.negative, self.window - ) - - @classmethod - def load(cls, *args, **kwargs): - """Load a previously saved object (using :meth:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.save`) from file. - - Also initializes extra instance attributes in case the loaded model does not include them. - `*args` or `**kwargs` **MUST** include the fname argument (path to saved file). - See :meth:`~gensim.utils.SaveLoad.load`. - - Parameters - ---------- - *args : object - Positional arguments passed to :meth:`~gensim.utils.SaveLoad.load`. - **kwargs : object - Key word arguments passed to :meth:`~gensim.utils.SaveLoad.load`. - - See Also - -------- - :meth:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.save` - Method for save a model. - - Returns - ------- - :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` - Model loaded from disk. - - Raises - ------ - IOError - When methods are called on instance (should be called from class). - - """ - model = super(BaseWordEmbeddingsModel, cls).load(*args, **kwargs) - if not hasattr(model, 'ns_exponent'): - model.ns_exponent = 0.75 - if not hasattr(model.vocabulary, 'ns_exponent'): - model.vocabulary.ns_exponent = 0.75 - if model.negative and hasattr(model.wv, 'index2word'): - model.vocabulary.make_cum_table(model.wv) # rebuild cum_table from vocabulary - if not hasattr(model, 'corpus_count'): - model.corpus_count = None - if not hasattr(model, 'corpus_total_words'): - model.corpus_total_words = None - if not hasattr(model.trainables, 'vectors_lockf') and hasattr(model.wv, 'vectors'): - model.trainables.vectors_lockf = ones(len(model.wv.vectors), dtype=REAL) - if not hasattr(model, 'random'): - model.random = random.RandomState(model.trainables.seed) - if not hasattr(model, 'train_count'): - model.train_count = 0 - model.total_train_time = 0 - return model - - def _log_progress(self, job_queue, progress_queue, cur_epoch, example_count, total_examples, - raw_word_count, total_words, trained_word_count, elapsed): - """Callback used to log progress for long running jobs. - - Parameters - ---------- - job_queue : Queue of (list of object, dict of (str, float)) - The queue of jobs still to be performed by workers. Each job is represented as a tuple containing - the batch of data to be processed and the parameters to be used for the processing as a dict. - progress_queue : Queue of (int, int, int) - A queue of progress reports. Each report is represented as a tuple of these 3 elements: - * size of data chunk processed, for example number of sentences in the corpus chunk. - * Effective word count used in training (after ignoring unknown words and trimming the sentence length). - * Total word count used in training. - cur_epoch : int - The current training iteration through the corpus. - example_count : int - Number of examples (could be sentences for example) processed until now. - total_examples : int - Number of all examples present in the input corpus. - raw_word_count : int - Number of words used in training until now. - total_words : int - Number of all words in the input corpus. - trained_word_count : int - Number of effective words used in training until now (after ignoring unknown words and trimming - the sentence length). - elapsed : int - Elapsed time since the beginning of training in seconds. - - Notes - ----- - If you train the model via `corpus_file` argument, there is no job_queue, so reported job_queue size will - always be equal to -1. - - """ - if total_examples: - # examples-based progress % - logger.info( - "EPOCH %i - PROGRESS: at %.2f%% examples, %.0f words/s, in_qsize %i, out_qsize %i", - cur_epoch + 1, 100.0 * example_count / total_examples, trained_word_count / elapsed, - -1 if job_queue is None else utils.qsize(job_queue), utils.qsize(progress_queue) - ) - else: - # words-based progress % - logger.info( - "EPOCH %i - PROGRESS: at %.2f%% words, %.0f words/s, in_qsize %i, out_qsize %i", - cur_epoch + 1, 100.0 * raw_word_count / total_words, trained_word_count / elapsed, - -1 if job_queue is None else utils.qsize(job_queue), utils.qsize(progress_queue) - ) - - def _log_epoch_end(self, cur_epoch, example_count, total_examples, raw_word_count, total_words, - trained_word_count, elapsed, is_corpus_file_mode): - """Callback used to log the end of a training epoch. - - Parameters - ---------- - cur_epoch : int - The current training iteration through the corpus. - example_count : int - Number of examples (could be sentences for example) processed until now. - total_examples : int - Number of all examples present in the input corpus. - raw_word_count : int - Number of words used in training until now. - total_words : int - Number of all words in the input corpus. - trained_word_count : int - Number of effective words used in training until now (after ignoring unknown words and trimming - the sentence length). - elapsed : int - Elapsed time since the beginning of training in seconds. - is_corpus_file_mode : bool - Whether training is file-based (corpus_file argument) or not. - - Warnings - -------- - In case the corpus is changed while the epoch was running. - - """ - logger.info( - "EPOCH - %i : training on %i raw words (%i effective words) took %.1fs, %.0f effective words/s", - cur_epoch + 1, raw_word_count, trained_word_count, elapsed, trained_word_count / elapsed - ) - - # don't warn if training in file-based mode, because it's expected behavior - if is_corpus_file_mode: - return - - # check that the input corpus hasn't changed during iteration - if total_examples and total_examples != example_count: - logger.warning( - "EPOCH - %i : supplied example count (%i) did not equal expected count (%i)", cur_epoch + 1, - example_count, total_examples - ) - if total_words and total_words != raw_word_count: - logger.warning( - "EPOCH - %i : supplied raw word count (%i) did not equal expected count (%i)", cur_epoch + 1, - raw_word_count, total_words - ) - - def _log_train_end(self, raw_word_count, trained_word_count, total_elapsed, job_tally): - """Callback to log the end of training. - - Parameters - ---------- - raw_word_count : int - Number of words used in the whole training. - trained_word_count : int - Number of effective words used in training (after ignoring unknown words and trimming the sentence length). - total_elapsed : int - Total time spent during training in seconds. - job_tally : int - Total number of jobs processed during training. - - """ - logger.info( - "training on a %i raw words (%i effective words) took %.1fs, %.0f effective words/s", - raw_word_count, trained_word_count, total_elapsed, trained_word_count / total_elapsed - ) diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index dd77348c8f..cefdd33091 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -569,7 +569,7 @@ def on_epoch_end(self, epoch, topics=None): class CallbackAny2Vec(object): - """Base class to build callbacks for :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel`. + """Base class to build callbacks for :class:`~gensim.models.word2vec.Word2Vec` & subclasses. Callbacks are used to apply custom functions over the model at specific points during training (epoch start, batch end etc.). This is a base class and its purpose is to be inherited by @@ -584,7 +584,7 @@ def on_epoch_begin(self, model): Parameters ---------- - model : :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` + model : :class:`~gensim.models.word2vec.Word2Vec` or subclass Current model. """ @@ -595,7 +595,7 @@ def on_epoch_end(self, model): Parameters ---------- - model : :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` + model : :class:`~gensim.models.word2vec.Word2Vec` or subclass Current model. """ @@ -606,7 +606,7 @@ def on_batch_begin(self, model): Parameters ---------- - model : :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` + model : :class:`~gensim.models.word2vec.Word2Vec` or subclass Current model. """ @@ -617,7 +617,7 @@ def on_batch_end(self, model): Parameters ---------- - model : :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` + model : :class:`~gensim.models.word2vec.Word2Vec` or subclass Current model. """ @@ -628,7 +628,7 @@ def on_train_begin(self, model): Parameters ---------- - model : :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` + model : :class:`~gensim.models.word2vec.Word2Vec` or subclass Current model. """ @@ -639,7 +639,7 @@ def on_train_end(self, model): Parameters ---------- - model : :class:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel` + model : :class:`~gensim.models.word2vec.Word2Vec` or subclass Current model. """ diff --git a/gensim/models/deprecated/__init__.py b/gensim/models/deprecated/__init__.py deleted file mode 100644 index cfa71654f5..0000000000 --- a/gensim/models/deprecated/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""This package contains some deprecated implementations of algorithm, will be removed soon.""" diff --git a/gensim/models/deprecated/doc2vec.py b/gensim/models/deprecated/doc2vec.py deleted file mode 100644 index 41f74fdc6b..0000000000 --- a/gensim/models/deprecated/doc2vec.py +++ /dev/null @@ -1,1044 +0,0 @@ -#!/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.:: - -.. sourcecode:: pycon - - >>> model = Doc2Vec(documents, size=100, window=8, min_count=5, workers=4) - -Persist a model to disk with:: - -.. sourcecode:: pycon - - >>> 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 - -.. sourcecode:: pycon - - >>> 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 zip, range -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), - 'vector_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, - 'epochs': 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 - # now upconvert that to gensim-4.0.0+ - new_model.docvecs._upconvert_old_d2vkv() - - new_model.train_count = old_model.__dict__.get('train_count', None) - new_model.corpus_count = old_model.__dict__.get('corpus_count', None) - new_model.corpus_total_words = old_model.__dict__.get('corpus_total_words', 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. - - .. sourcecode:: pycon - - >>> 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 range(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 range(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.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 - with utils.open(fname, 'rb') as f: - for item_no, line in enumerate(f): - 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.open(self.source, 'rb') as fin: - for item_no, line in enumerate(fin): - yield TaggedDocument(utils.to_unicode(line).split(), [item_no]) diff --git a/gensim/models/deprecated/fasttext.py b/gensim/models/deprecated/fasttext.py deleted file mode 100644 index 0d46b6f1cc..0000000000 --- a/gensim/models/deprecated/fasttext.py +++ /dev/null @@ -1,711 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# Authors: Chinmaya Pancholi , Shiva Manne -# Copyright (C) 2017 RaRe Technologies s.r.o. - -""" -Warnings --------- -.. deprecated:: 3.3.0 - Use :mod:`gensim.models.fasttext` instead. - - -Learn word representations via fasttext's "skip-gram and CBOW models", using either -hierarchical softmax or negative sampling [1]_. - -Notes ------ -There are more ways to get word vectors in Gensim than just FastText. -See wrappers for VarEmbed and WordRank or Word2Vec - -This module allows training a word embedding from a training corpus with the additional ability -to obtain word vectors for out-of-vocabulary words. - -For a tutorial on gensim's native fasttext, refer to the noteboook -- [2]_ - -**Make sure you have a C compiler before installing gensim, to use optimized (compiled) fasttext training** - -.. [1] P. Bojanowski, E. Grave, A. Joulin, T. Mikolov - Enriching Word Vectors with Subword Information. In arXiv preprint arXiv:1607.04606. - https://arxiv.org/abs/1607.04606 - -.. [2] https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/FastText_Tutorial.ipynb - -""" - -import logging - -import numpy as np -from numpy import zeros, ones, vstack, sum as np_sum, empty, float32 as REAL - -from gensim.models.deprecated.word2vec import Word2Vec, train_sg_pair, train_cbow_pair -from gensim.models.deprecated.fasttext_wrapper import FastTextKeyedVectors -from gensim.models.deprecated.fasttext_wrapper import FastText as Ft_Wrapper, compute_ngrams, ft_hash -from gensim.models.fasttext import FastText as NewFastText - -logger = logging.getLogger(__name__) - -MAX_WORDS_IN_BATCH = 10000 - - -def load_old_fasttext(*args, **kwargs): - old_model = FastText.load(*args, **kwargs) - params = { - '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, - 'sg': old_model.sg, - 'hs': old_model.hs, - 'negative': old_model.negative, - 'cbow_mean': old_model.cbow_mean, - 'hashfxn': old_model.hashfxn, - 'iter': old_model.iter, - 'null_word': old_model.null_word, - 'sorted_vocab': old_model.sorted_vocab, - 'batch_words': old_model.batch_words, - 'min_n': old_model.min_n, - 'max_n': old_model.max_n, - 'word_ngrams': old_model.word_ngrams, - 'bucket': old_model.bucket - } - new_model = NewFastText(**params) - # set trainables attributes - new_model.wv.vectors = old_model.wv.syn0 - new_model.wv.vectors_vocab = old_model.wv.syn0_vocab - new_model.wv.vectors_ngrams = old_model.wv.syn0_ngrams - if hasattr(old_model.wv, 'syn0norm'): - new_model.wv.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 - - if hasattr(old_model, 'syn0_vocab_lockf'): - new_model.trainables.vectors_vocab_lockf = old_model.syn0_vocab_lockf - if hasattr(old_model, 'syn0_ngrams_lockf'): - new_model.trainables.vectors_ngrams_lockf = old_model.syn0_ngrams_lockf - if hasattr(old_model.wv, 'syn0_vocab_norm'): - new_model.trainables.vectors_vocab_norm = old_model.wv.syn0_vocab_norm - if hasattr(old_model.wv, 'syn0_ngrams_norm'): - new_model.trainables.vectors_ngrams_norm = old_model.wv.syn0_ngrams_norm - - # set 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 - - new_model.wv.hash2index = old_model.wv.hash2index - - new_model.train_count = old_model.train_count - new_model.corpus_count = old_model.corpus_count - new_model.corpus_total_words = old_model.corpus_total_words - new_model.running_training_loss = old_model.running_training_loss - new_model.total_train_time = old_model.total_train_time - new_model.min_alpha_yet_reached = old_model.min_alpha_yet_reached - new_model.model_trimmed_post_training = old_model.model_trimmed_post_training - - new_model.trainables.num_ngram_vectors = old_model.num_ngram_vectors - - return new_model - - -def train_batch_cbow(model, sentences, alpha, work=None, neu1=None): - """Update CBOW model by training on a sequence of sentences. - - Each sentence is a list of string tokens, which are looked up in the model's - vocab dictionary. Called internally from :meth:`gensim.models.fasttext.FastText.train()`. - - This is the non-optimized, Python version. If you have cython installed, gensim - will use the optimized version from fasttext_inner instead. - - Parameters - ---------- - model : :class:`~gensim.models.fasttext.FastText` - `FastText` instance. - sentences : iterable of iterables - Iterable of the sentences directly from disk/network. - alpha : float - Learning rate. - work : :class:`numpy.ndarray` - Private working memory for each worker. - neu1 : :class:`numpy.ndarray` - Private working memory for each worker. - - Returns - ------- - int - Effective number of words trained. - - """ - result = 0 - for sentence in sentences: - word_vocabs = [model.wv.vocab[w] for w in sentence 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) - start = max(0, pos - model.window + reduced_window) - window_pos = enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start) - word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)] - - word2_subwords = [] - vocab_subwords_indices = [] - ngrams_subwords_indices = [] - - for index in word2_indices: - vocab_subwords_indices += [index] - word2_subwords += model.wv.ngrams_word[model.wv.index2word[index]] - - for subword in word2_subwords: - ngrams_subwords_indices.append(model.wv.ngrams[subword]) - - l1_vocab = np_sum(model.wv.syn0_vocab[vocab_subwords_indices], axis=0) # 1 x vector_size - l1_ngrams = np_sum(model.wv.syn0_ngrams[ngrams_subwords_indices], axis=0) # 1 x vector_size - - l1 = np_sum([l1_vocab, l1_ngrams], axis=0) - subwords_indices = [vocab_subwords_indices] + [ngrams_subwords_indices] - if (subwords_indices[0] or subwords_indices[1]) and model.cbow_mean: - l1 /= (len(subwords_indices[0]) + len(subwords_indices[1])) - - # train on the sliding window for target word - train_cbow_pair(model, word, subwords_indices, l1, alpha, is_ft=True) - result += len(word_vocabs) - return result - - -def train_batch_sg(model, sentences, alpha, work=None, neu1=None): - """Update skip-gram model by training on a sequence of sentences. - - Each sentence is a list of string tokens, which are looked up in the model's - vocab dictionary. Called internally from :meth:`gensim.models.fasttext.FastText.train()`. - - This is the non-optimized, Python version. If you have cython installed, gensim - will use the optimized version from fasttext_inner instead. - - Parameters - ---------- - model : :class:`~gensim.models.fasttext.FastText` - `FastText` instance. - sentences : iterable of iterables - Iterable of the sentences directly from disk/network. - alpha : float - Learning rate. - work : :class:`numpy.ndarray` - Private working memory for each worker. - neu1 : :class:`numpy.ndarray` - Private working memory for each worker. - - Returns - ------- - int - Effective number of words trained. - - """ - result = 0 - for sentence in sentences: - word_vocabs = [model.wv.vocab[w] for w in sentence 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 word2vec code - # now go over all words from the (reduced) window, predicting each one in turn - start = max(0, pos - model.window + reduced_window) - - subwords_indices = [word.index] - word2_subwords = model.wv.ngrams_word[model.wv.index2word[word.index]] - - for subword in word2_subwords: - subwords_indices.append(model.wv.ngrams[subword]) - - for pos2, word2 in enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start): - if pos2 != pos: # don't train on the `word` itself - train_sg_pair(model, model.wv.index2word[word2.index], subwords_indices, alpha, is_ft=True) - - result += len(word_vocabs) - return result - - -class FastText(Word2Vec): - """Class for training, using and evaluating word representations learned using method - described in [1]_ aka Fasttext. - - The model can be stored/loaded via its :meth:`~gensim.models.fasttext.FastText.save()` and - :meth:`~gensim.models.fasttext.FastText.load()` methods, or loaded in a format compatible with the original - fasttext implementation via :meth:`~gensim.models.fasttext.FastText.load_fasttext_format()`. - - """ - def __init__( - self, sentences=None, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, - max_vocab_size=None, word_ngrams=1, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, - negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, - bucket=2000000, trim_rule=None, batch_words=MAX_WORDS_IN_BATCH): - """Initialize the model from an iterable of `sentences`. Each sentence is a - list of words (unicode strings) that will be used for training. - - Parameters - ---------- - sentences : iterable of iterables - The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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` in :mod:`~gensim.models.word2vec` module for such examples. - If you don't supply `sentences`, the model is left uninitialized -- use if you plan to initialize it - in some other way. - sg : int {1, 0} - Defines the training algorithm. If 1, skip-gram is used, otherwise, CBOW is employed. - size : int - Dimensionality of the feature vectors. - window : int - The maximum distance between the current and predicted word within a sentence. - alpha : float - The initial learning rate. - min_alpha : float - Learning rate will linearly drop to `min_alpha` as training progresses. - seed : int - Seed for the random number generator. Initial vectors for each word are seeded with a hash of - the concatenation of word + `str(seed)`. Note that for a fully deterministically-reproducible run, - you must also limit the model to a single worker thread (`workers=1`), 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 : int - Ignores all words with total frequency lower than this. - max_vocab_size : int - Limits the 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. - sample : float - The threshold for configuring which higher-frequency words are randomly downsampled, - useful range is (0, 1e-5). - workers : int - Use these many worker threads to train the model (=faster training with multicore machines). - hs : int {1,0} - If 1, hierarchical softmax will be used for model training. - If set to 0, and `negative` is non-zero, negative sampling will be used. - negative : int - If > 0, negative sampling will be used, the int for negative specifies how many "noise words" - should be drawn (usually between 5-20). - If set to 0, no negative sampling is used. - cbow_mean : int {1,0} - If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used. - hashfxn : function - Hash function to use to randomly initialize weights, for increased training reproducibility. - iter : int - Number of iterations (epochs) over the corpus. - trim_rule : function - 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, look to :func:`~gensim.utils.keep_vocab_item`), - or a callable that accepts parameters (word, count, min_count) and returns either - :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`. - Note: The rule, if given, is only used to prune vocabulary during build_vocab() and is not stored as part - of the model. - sorted_vocab : int {1,0} - If 1, sort the vocabulary by descending frequency before assigning word indexes. - batch_words : int - Target size (in words) for batches of examples passed to worker threads (and - thus cython routines).(Larger batches will be passed if individual - texts are longer than 10000 words, but the standard cython code truncates to that maximum.) - min_n : int - Min length of char ngrams to be used for training word representations. - max_n : int - Max length of char ngrams to be used for training word representations. Set `max_n` to be - lesser than `min_n` to avoid char ngrams being used. - word_ngrams : int {1,0} - If 1, uses enriches word vectors with subword(ngrams) information. - If 0, this is equivalent to word2vec. - bucket : int - Character ngrams are hashed into a fixed number of buckets, in order to limit the - memory usage of the model. This option specifies the number of buckets used by the model. - - Examples - -------- - Initialize and train a `FastText` model - - .. sourcecode:: pycon - - >>> from gensim.models import FastText - >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] - >>> - >>> model = FastText(sentences, min_count=1) - >>> say_vector = model['say'] # get vector for word - >>> of_vector = model['of'] # get vector for out-of-vocab word - - """ - # fastText specific params - self.bucket = bucket - self.word_ngrams = word_ngrams - self.min_n = min_n - self.max_n = max_n - if self.word_ngrams <= 1 and self.max_n == 0: - self.bucket = 0 - - super(FastText, self).__init__( - sentences=sentences, size=size, alpha=alpha, window=window, min_count=min_count, - max_vocab_size=max_vocab_size, sample=sample, seed=seed, workers=workers, min_alpha=min_alpha, - sg=sg, hs=hs, negative=negative, cbow_mean=cbow_mean, hashfxn=hashfxn, iter=iter, null_word=null_word, - trim_rule=trim_rule, sorted_vocab=sorted_vocab, batch_words=batch_words) - - def initialize_word_vectors(self): - """Initializes FastTextKeyedVectors instance to store all vocab/ngram vectors for the model.""" - self.wv = FastTextKeyedVectors() - self.wv.min_n = self.min_n - self.wv.max_n = self.max_n - - def build_vocab(self, sentences, keep_raw_vocab=False, trim_rule=None, progress_per=10000, update=False): - """Build vocabulary from a sequence of sentences (can be a once-only generator stream). - Each sentence must be a list of unicode strings. - - Parameters - ---------- - sentences : iterable of iterables - The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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` in :mod:`~gensim.models.word2vec` module for such examples. - keep_raw_vocab : bool - If not true, delete the raw vocabulary after the scaling is done and free up RAM. - trim_rule : function - 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, look to :func:`~gensim.utils.keep_vocab_item`), - or a callable that accepts parameters (word, count, min_count) and returns either - :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`. - Note: The rule, if given, is only used to prune vocabulary during build_vocab() and is not stored as part - of the model. - progress_per : int - Indicates how many words to process before showing/updating the progress. - update: bool - If true, the new words in `sentences` will be added to model's vocab. - - Example - ------- - Train a model and update vocab for online training - - .. sourcecode:: pycon - - >>> from gensim.models import FastText - >>> sentences_1 = [["cat", "say", "meow"], ["dog", "say", "woof"]] - >>> sentences_2 = [["dude", "say", "wazzup!"]] - >>> - >>> model = FastText(min_count=1) - >>> model.build_vocab(sentences_1) - >>> model.train(sentences_1, total_examples=model.corpus_count, epochs=model.iter) - >>> model.build_vocab(sentences_2, update=True) - >>> model.train(sentences_2, total_examples=model.corpus_count, epochs=model.iter) - - """ - if update: - if not len(self.wv.vocab): - raise RuntimeError( - "You cannot do an online vocabulary-update of a model which has no prior vocabulary. " - "First build the vocabulary of your model with a corpus " - "before doing an online update.") - self.old_vocab_len = len(self.wv.vocab) - self.old_hash2index_len = len(self.wv.hash2index) - - super(FastText, self).build_vocab( - sentences, keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, progress_per=progress_per, update=update) - self.init_ngrams(update=update) - - def init_ngrams(self, update=False): - """Compute ngrams of all words present in vocabulary and stores vectors for only those ngrams. - Vectors for other ngrams are initialized with a random uniform distribution in FastText. - - Parameters - ---------- - update : bool - If True, the new vocab words and their new ngrams word vectors are initialized - with random uniform distribution and updated/added to the existing vocab word and ngram vectors. - - """ - if not update: - self.wv.ngrams = {} - self.wv.syn0_vocab = empty((len(self.wv.vocab), self.vector_size), dtype=REAL) - self.syn0_vocab_lockf = ones((len(self.wv.vocab), self.vector_size), dtype=REAL) - - self.wv.syn0_ngrams = empty((self.bucket, self.vector_size), dtype=REAL) - self.syn0_ngrams_lockf = ones((self.bucket, self.vector_size), dtype=REAL) - - all_ngrams = [] - for w, v in self.wv.vocab.items(): - self.wv.ngrams_word[w] = compute_ngrams(w, self.min_n, self.max_n) - all_ngrams += self.wv.ngrams_word[w] - - all_ngrams = list(set(all_ngrams)) - self.num_ngram_vectors = len(all_ngrams) - logger.info("Total number of ngrams is %d", len(all_ngrams)) - - self.wv.hash2index = {} - ngram_indices = [] - new_hash_count = 0 - for i, ngram in enumerate(all_ngrams): - ngram_hash = ft_hash(ngram) % self.bucket - if ngram_hash in self.wv.hash2index: - self.wv.ngrams[ngram] = self.wv.hash2index[ngram_hash] - else: - ngram_indices.append(ngram_hash % self.bucket) - self.wv.hash2index[ngram_hash] = new_hash_count - self.wv.ngrams[ngram] = self.wv.hash2index[ngram_hash] - new_hash_count = new_hash_count + 1 - - self.wv.syn0_ngrams = self.wv.syn0_ngrams.take(ngram_indices, axis=0) - self.syn0_ngrams_lockf = self.syn0_ngrams_lockf.take(ngram_indices, axis=0) - self.reset_ngram_weights() - else: - new_ngrams = [] - for w, v in self.wv.vocab.items(): - self.wv.ngrams_word[w] = compute_ngrams(w, self.min_n, self.max_n) - new_ngrams += [ng for ng in self.wv.ngrams_word[w] if ng not in self.wv.ngrams] - - new_ngrams = list(set(new_ngrams)) - logger.info("Number of new ngrams is %d", len(new_ngrams)) - new_hash_count = 0 - for i, ngram in enumerate(new_ngrams): - ngram_hash = ft_hash(ngram) % self.bucket - if ngram_hash not in self.wv.hash2index: - self.wv.hash2index[ngram_hash] = new_hash_count + self.old_hash2index_len - self.wv.ngrams[ngram] = self.wv.hash2index[ngram_hash] - new_hash_count = new_hash_count + 1 - else: - self.wv.ngrams[ngram] = self.wv.hash2index[ngram_hash] - - rand_obj = np.random - rand_obj.seed(self.seed) - new_vocab_rows = rand_obj.uniform( - -1.0 / self.vector_size, 1.0 / self.vector_size, - (len(self.wv.vocab) - self.old_vocab_len, self.vector_size) - ).astype(REAL) - new_vocab_lockf_rows = ones((len(self.wv.vocab) - self.old_vocab_len, self.vector_size), dtype=REAL) - new_ngram_rows = rand_obj.uniform( - -1.0 / self.vector_size, 1.0 / self.vector_size, - (len(self.wv.hash2index) - self.old_hash2index_len, self.vector_size) - ).astype(REAL) - new_ngram_lockf_rows = ones( - (len(self.wv.hash2index) - self.old_hash2index_len, - self.vector_size), - dtype=REAL) - - self.wv.syn0_vocab = vstack([self.wv.syn0_vocab, new_vocab_rows]) - self.syn0_vocab_lockf = vstack([self.syn0_vocab_lockf, new_vocab_lockf_rows]) - self.wv.syn0_ngrams = vstack([self.wv.syn0_ngrams, new_ngram_rows]) - self.syn0_ngrams_lockf = vstack([self.syn0_ngrams_lockf, new_ngram_lockf_rows]) - - def reset_ngram_weights(self): - """Reset all projection weights to an initial (untrained) state, - but keep the existing vocabulary and their ngrams. - - """ - rand_obj = np.random - rand_obj.seed(self.seed) - for index in range(len(self.wv.vocab)): - self.wv.syn0_vocab[index] = rand_obj.uniform( - -1.0 / self.vector_size, 1.0 / self.vector_size, self.vector_size - ).astype(REAL) - for index in range(len(self.wv.hash2index)): - self.wv.syn0_ngrams[index] = rand_obj.uniform( - -1.0 / self.vector_size, 1.0 / self.vector_size, self.vector_size - ).astype(REAL) - - def _do_train_job(self, sentences, alpha, inits): - """Train a single batch of sentences. Return 2-tuple `(effective word count after - ignoring unknown words and sentence length trimming, total word count)`. - - Parameters - ---------- - sentences : iterable of iterables - The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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` in :mod:`~gensim.models.word2vec` module for such examples. - alpha : float - The current learning rate. - inits : (:class:`numpy.ndarray`, :class:`numpy.ndarray`) - Each worker's private work memory. - - Returns - ------- - (int, int) - Tuple of (effective word count after ignoring unknown words and sentence length trimming, total word count) - - """ - work, neu1 = inits - tally = 0 - if self.sg: - tally += train_batch_sg(self, sentences, alpha, work, neu1) - else: - tally += train_batch_cbow(self, sentences, alpha, work, neu1) - - return tally, self._raw_word_count(sentences) - - def train(self, sentences, total_examples=None, total_words=None, - epochs=None, start_alpha=None, end_alpha=None, - word_count=0, queue_factor=2, report_delay=1.0): - """Update the model's neural weights from a sequence of sentences (can be a once-only generator stream). - For FastText, each sentence must be a list of unicode strings. (Subclasses may accept other examples.) - - To support linear learning-rate decay from (initial) alpha to min_alpha, and accurate - progress-percentage logging, either total_examples (count of sentences) or total_words (count of - raw words in sentences) **MUST** be provided (if the corpus is the same as was provided to - :meth:`~gensim.models.fasttext.FastText.build_vocab()`, the count of examples in that corpus - will be available in the model's :attr:`corpus_count` property). - - To avoid common mistakes around the model's ability to do multiple training passes itself, an - explicit `epochs` argument **MUST** be provided. In the common and recommended case, - where :meth:`~gensim.models.fasttext.FastText.train()` is only called once, - the model's cached `iter` value should be supplied as `epochs` value. - - Parameters - ---------- - sentences : iterable of iterables - The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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` in :mod:`~gensim.models.word2vec` module for such examples. - total_examples : int - Count of sentences. - total_words : int - Count of raw words in sentences. - epochs : int - Number of iterations (epochs) over the corpus. - start_alpha : float - Initial learning rate. - end_alpha : float - Final learning rate. Drops linearly from `start_alpha`. - word_count : int - Count of words already trained. Set this to 0 for the usual - case of training on all words in sentences. - queue_factor : int - Multiplier for size of queue (number of workers * queue_factor). - report_delay : float - Seconds to wait before reporting progress. - - Examples - -------- - - .. sourcecode:: pycon - - >>> from gensim.models import FastText - >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] - >>> - >>> model = FastText(min_count=1) - >>> model.build_vocab(sentences) - >>> model.train(sentences, total_examples=model.corpus_count, epochs=model.iter) - - """ - self.neg_labels = [] - if self.negative > 0: - # precompute negative labels optimization for pure-python training - self.neg_labels = zeros(self.negative + 1) - self.neg_labels[0] = 1. - - Word2Vec.train( - self, sentences, total_examples=self.corpus_count, epochs=self.iter, - start_alpha=self.alpha, end_alpha=self.min_alpha) - self.get_vocab_word_vecs() - - def __getitem__(self, word): - """Get `word` representations in vector space, as a 1D numpy array. - - Parameters - ---------- - word : str - A single word whose vector needs to be returned. - - Returns - ------- - :class:`numpy.ndarray` - The word's representations in vector space, as a 1D numpy array. - - Raises - ------ - KeyError - For words with all ngrams absent, a KeyError is raised. - - Example - ------- - .. sourcecode:: pycon - - >>> from gensim.models import FastText - >>> from gensim.test.utils import datapath - >>> - >>> trained_model = FastText.load_fasttext_format(datapath('lee_fasttext')) - >>> meow_vector = trained_model['hello'] # get vector for word - - """ - return self.word_vec(word) - - def get_vocab_word_vecs(self): - """Calculate vectors for words in vocabulary and stores them in `wv.syn0`.""" - for w, v in self.wv.vocab.items(): - word_vec = np.copy(self.wv.syn0_vocab[v.index]) - ngrams = self.wv.ngrams_word[w] - ngram_weights = self.wv.syn0_ngrams - for ngram in ngrams: - word_vec += ngram_weights[self.wv.ngrams[ngram]] - word_vec /= (len(ngrams) + 1) - self.wv.syn0[v.index] = word_vec - - def word_vec(self, word, use_norm=False): - """Get the word's representations in vector space, as a 1D numpy array. - - Parameters - ---------- - word : str - A single word whose vector needs to be returned. - use_norm : bool - If True, returns normalized vector. - - Returns - ------- - :class:`numpy.ndarray` - The word's representations in vector space, as a 1D numpy array. - - Raises - ------ - KeyError - For words with all ngrams absent, a KeyError is raised. - - Example - ------- - .. sourcecode:: pycon - - >>> from gensim.models import FastText - >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] - >>> - >>> model = FastText(sentences, min_count=1) - >>> meow_vector = model.word_vec('meow') # get vector for word - - """ - return FastTextKeyedVectors.word_vec(self.wv, word, use_norm=use_norm) - - @classmethod - def load_fasttext_format(cls, *args, **kwargs): - """Load a :class:`~gensim.models.fasttext.FastText` model from a format compatible with - the original fasttext implementation. - - Parameters - ---------- - fname : str - Path to the file. - - """ - return Ft_Wrapper.load_fasttext_format(*args, **kwargs) - - def save(self, *args, **kwargs): - """Save the model. This saved model can be loaded again using :func:`~gensim.models.fasttext.FastText.load`, - which supports online training and getting vectors for out-of-vocabulary words. - - Parameters - ---------- - fname : str - Path to the file. - - """ - kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_vocab_norm', 'syn0_ngrams_norm']) - super(FastText, self).save(*args, **kwargs) diff --git a/gensim/models/deprecated/fasttext_wrapper.py b/gensim/models/deprecated/fasttext_wrapper.py deleted file mode 100644 index 727db0e1e0..0000000000 --- a/gensim/models/deprecated/fasttext_wrapper.py +++ /dev/null @@ -1,461 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Author: Jayant Jain -# Copyright (C) 2017 Radim Rehurek -# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html - - -""" -Warnings --------- -.. deprecated:: 3.2.0 - Use :mod:`gensim.models.fasttext` instead. - - -Python wrapper around word representation learning from FastText, a library for efficient learning -of word representations and sentence classification [1]. - -This module allows training a word embedding from a training corpus with the additional ability -to obtain word vectors for out-of-vocabulary words, using the fastText C implementation. - -The wrapped model can NOT be updated with new documents for online training -- use gensim's -`Word2Vec` for that. - -Example: -.. sourcecode:: pycon - - >>> from gensim.models.wrappers import FastText - >>> model = FastText.train('/Users/kofola/fastText/fasttext', corpus_file='text8') - >>> print model['forests'] # prints vector for given out-of-vocabulary word - -.. [1] https://github.com/facebookresearch/fastText#enriching-word-vectors-with-subword-information - - - -""" - - -import logging -import tempfile -import os -import struct - -import numpy as np -from numpy import float32 as REAL, sqrt, newaxis -from gensim import utils -from gensim.models.deprecated.keyedvectors import KeyedVectors, Vocab -from gensim.models.deprecated.word2vec import Word2Vec - -logger = logging.getLogger(__name__) - -try: - FileNotFoundError -except NameError: - FileNotFoundError = IOError - -FASTTEXT_FILEFORMAT_MAGIC = 793712314 - - -class FastTextKeyedVectors(KeyedVectors): - """ - Class to contain vectors, vocab and ngrams for the FastText training class and other methods not directly - involved in training such as most_similar(). - Subclasses KeyedVectors to implement oov lookups, storing ngrams and other FastText specific methods - - """ - - def __init__(self): - super(FastTextKeyedVectors, self).__init__() - self.syn0_vocab = None - self.syn0_vocab_norm = None - self.syn0_ngrams = None - self.syn0_ngrams_norm = None - self.ngrams = {} - self.hash2index = {} - self.ngrams_word = {} - self.min_n = 0 - self.max_n = 0 - - def save(self, *args, **kwargs): - # don't bother storing the cached normalized vectors - kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_vocab_norm', 'syn0_ngrams_norm']) - super(FastTextKeyedVectors, self).save(*args, **kwargs) - - def word_vec(self, word, use_norm=False): - """ - Accept a single word as input. - Returns the word's representations in vector space, as a 1D numpy array. - - The word can be out-of-vocabulary as long as ngrams for the word are present. - For words with all ngrams absent, a KeyError is raised. - - Example: - - .. sourcecode:: pycon - - >>> trained_model['office'] - array([ -1.40128313e-02, ...]) - - """ - if word in self.vocab: - return super(FastTextKeyedVectors, self).word_vec(word, use_norm) - else: - word_vec = np.zeros(self.syn0_ngrams.shape[1], dtype=np.float32) - ngrams = compute_ngrams(word, self.min_n, self.max_n) - ngrams = [ng for ng in ngrams if ng in self.ngrams] - if use_norm: - ngram_weights = self.syn0_ngrams_norm - else: - ngram_weights = self.syn0_ngrams - for ngram in ngrams: - word_vec += ngram_weights[self.ngrams[ngram]] - if word_vec.any(): - return word_vec / len(ngrams) - 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. - - 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 only call `most_similar`, `similarity` etc. - - """ - super(FastTextKeyedVectors, self).init_sims(replace) - if getattr(self, 'syn0_ngrams_norm', None) is None or replace: - logger.info("precomputing L2-norms of ngram weight vectors") - if replace: - for i in range(self.syn0_ngrams.shape[0]): - self.syn0_ngrams[i, :] /= sqrt((self.syn0_ngrams[i, :] ** 2).sum(-1)) - self.syn0_ngrams_norm = self.syn0_ngrams - else: - self.syn0_ngrams_norm = \ - (self.syn0_ngrams / sqrt((self.syn0_ngrams ** 2).sum(-1))[..., newaxis]).astype(REAL) - - 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 `__contains__` returns True. - """ - if word in self.vocab: - return True - else: - char_ngrams = compute_ngrams(word, self.min_n, self.max_n) - return any(ng in self.ngrams for ng in char_ngrams) - - @classmethod - def load_word2vec_format(cls, *args, **kwargs): - """Not suppported. Use gensim.models.KeyedVectors.load_word2vec_format instead.""" - raise NotImplementedError("Not supported. Use gensim.models.KeyedVectors.load_word2vec_format instead.") - - -class FastText(Word2Vec): - """ - Class for word vector training using FastText. Communication between FastText and Python - takes place by working with data files on disk and calling the FastText binary with - subprocess.call(). - Implements functionality similar to [fasttext.py](https://github.com/salestock/fastText.py), - improving speed and scope of functionality like `most_similar`, `similarity` by extracting vectors - into numpy matrix. - - Warnings - -------- - .. deprecated:: 3.2.0 - Use :class:`gensim.models.fasttext.FastText` instead of :class:`gensim.models.wrappers.fasttext.FastText`. - - - """ - - def initialize_word_vectors(self): - self.wv = FastTextKeyedVectors() - - @classmethod - def train(cls, ft_path, corpus_file, output_file=None, model='cbow', size=100, alpha=0.025, window=5, min_count=5, - word_ngrams=1, loss='ns', sample=1e-3, negative=5, iter=5, min_n=3, max_n=6, sorted_vocab=1, threads=12): - """ - `ft_path` is the path to the FastText executable, e.g. `/home/kofola/fastText/fasttext`. - - `corpus_file` is the filename of the text file to be used for training the FastText model. - Expects file to contain utf-8 encoded text. - - `model` defines the training algorithm. By default, cbow is used. Accepted values are - 'cbow', 'skipgram'. - - `size` is the dimensionality of the feature vectors. - - `window` is the maximum distance between the current and predicted word within a sentence. - - `alpha` is the initial learning rate. - - `min_count` = ignore all words with total occurrences lower than this. - - `word_ngram` = max length of word ngram - - `loss` = defines training objective. Allowed values are `hs` (hierarchical softmax), - `ns` (negative sampling) and `softmax`. Defaults to `ns` - - `sample` = threshold for configuring which higher-frequency words are randomly downsampled; - default is 1e-3, useful range is (0, 1e-5). - - `negative` = the value 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. - Only relevant when `loss` is set to `ns` - - `iter` = number of iterations (epochs) over the corpus. Default is 5. - - `min_n` = min length of char ngrams to be used for training word representations. Default is 3. - - `max_n` = max length of char ngrams to be used for training word representations. Set `max_n` to be - lesser than `min_n` to avoid char ngrams being used. Default is 6. - - `sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before - assigning word indexes. - - `threads` = number of threads to use. Default is 12. - - """ - ft_path = ft_path - output_file = output_file or os.path.join(tempfile.gettempdir(), 'ft_model') - ft_args = { - 'input': corpus_file, - 'output': output_file, - 'lr': alpha, - 'dim': size, - 'ws': window, - 'epoch': iter, - 'minCount': min_count, - 'wordNgrams': word_ngrams, - 'neg': negative, - 'loss': loss, - 'minn': min_n, - 'maxn': max_n, - 'thread': threads, - 't': sample - } - cmd = [ft_path, model] - for option, value in ft_args.items(): - cmd.append("-%s" % option) - cmd.append(str(value)) - - utils.check_output(args=cmd) - model = cls.load_fasttext_format(output_file) - cls.delete_training_files(output_file) - return model - - def save(self, *args, **kwargs): - # don't bother storing the cached normalized vectors - kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'syn0_vocab_norm', 'syn0_ngrams_norm']) - super(FastText, self).save(*args, **kwargs) - - @classmethod - def load_fasttext_format(cls, model_file, encoding='utf8'): - """ - Load the input-hidden weight matrix from the fast text output files. - - Note that due to limitations in the FastText API, you cannot continue training - with a model loaded this way, though you can query for word similarity etc. - - `model_file` is the path to the FastText output files. - FastText outputs two model files - `/path/to/model.vec` and `/path/to/model.bin` - - Expected value for this example: `/path/to/model` or `/path/to/model.bin`, - as gensim requires only `.bin` file to load entire fastText model. - - """ - model = cls() - if not model_file.endswith('.bin'): - model_file += '.bin' - model.file_name = model_file - model.load_binary_data(encoding=encoding) - return model - - @classmethod - def load(cls, *args, **kwargs): - model = super(FastText, cls).load(*args, **kwargs) - if hasattr(model.wv, 'syn0_all'): - setattr(model.wv, 'syn0_ngrams', model.wv.syn0_all) - delattr(model.wv, 'syn0_all') - return model - - @classmethod - def delete_training_files(cls, model_file): - """Deletes the files created by FastText training""" - try: - os.remove('%s.vec' % model_file) - os.remove('%s.bin' % model_file) - except FileNotFoundError: - logger.debug('Training files %s not found when attempting to delete', model_file) - pass - - def load_binary_data(self, encoding='utf8'): - """Loads data from the output binary file created by FastText training""" - - # TODO use smart_open again when https://github.com/RaRe-Technologies/smart_open/issues/207 will be fixed - with open(self.file_name, 'rb') as f: - self.load_model_params(f) - self.load_dict(f, encoding=encoding) - self.load_vectors(f) - - def load_model_params(self, file_handle): - magic, version = self.struct_unpack(file_handle, '@2i') - if magic == FASTTEXT_FILEFORMAT_MAGIC: # newer format - self.new_format = True - dim, ws, epoch, min_count, neg, _, loss, model, bucket, minn, maxn, _, t = \ - self.struct_unpack(file_handle, '@12i1d') - else: # older format - self.new_format = False - dim = magic - ws = version - epoch, min_count, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@10i1d') - # Parameters stored by [Args::save](https://github.com/facebookresearch/fastText/blob/master/src/args.cc) - self.vector_size = dim - self.window = ws - self.iter = epoch - self.min_count = min_count - self.negative = neg - self.hs = loss == 1 - self.sg = model == 2 - self.bucket = bucket - self.wv.min_n = minn - self.wv.max_n = maxn - self.sample = t - - def load_dict(self, file_handle, encoding='utf8'): - vocab_size, nwords, nlabels = self.struct_unpack(file_handle, '@3i') - # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - if nlabels > 0: - raise NotImplementedError("Supervised fastText models are not supported") - logger.info("loading %s words for fastText model from %s", vocab_size, self.file_name) - - self.struct_unpack(file_handle, '@1q') # number of tokens - if self.new_format: - pruneidx_size, = self.struct_unpack(file_handle, '@q') - for i in range(vocab_size): - word_bytes = b'' - char_byte = file_handle.read(1) - # Read vocab word - while char_byte != b'\x00': - word_bytes += char_byte - char_byte = file_handle.read(1) - word = word_bytes.decode(encoding) - count, _ = self.struct_unpack(file_handle, '@qb') - - self.wv.vocab[word] = Vocab(index=i, count=count) - self.wv.index2word.append(word) - - assert len(self.wv.vocab) == nwords, ( - 'mismatch between final vocab size ({} words), ' - 'and expected number of words ({} words)'.format(len(self.wv.vocab), nwords)) - if len(self.wv.vocab) != vocab_size: - # expecting to log this warning only for pretrained french vector, wiki.fr - logger.warning( - "mismatch between final vocab size (%s words), and expected vocab size (%s words)", - len(self.wv.vocab), vocab_size - ) - - if self.new_format: - for j in range(pruneidx_size): - self.struct_unpack(file_handle, '@2i') - - def load_vectors(self, file_handle): - if self.new_format: - self.struct_unpack(file_handle, '@?') # bool quant_input in fasttext.cc - num_vectors, dim = self.struct_unpack(file_handle, '@2q') - # Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc) - assert self.vector_size == dim, ( - 'mismatch between vector size in model params ({}) and model vectors ({})' - .format(self.vector_size, dim) - ) - float_size = struct.calcsize('@f') - if float_size == 4: - dtype = np.dtype(np.float32) - elif float_size == 8: - dtype = np.dtype(np.float64) - - self.num_original_vectors = num_vectors - self.wv.syn0_ngrams = np.fromfile(file_handle, dtype=dtype, count=num_vectors * dim) - self.wv.syn0_ngrams = self.wv.syn0_ngrams.reshape((num_vectors, dim)) - assert self.wv.syn0_ngrams.shape == (self.bucket + len(self.wv.vocab), self.vector_size), \ - 'mismatch between actual weight matrix shape {} and expected shape {}'\ - .format( - self.wv.syn0_ngrams.shape, (self.bucket + len(self.wv.vocab), self.vector_size) - ) - - self.init_ngrams() - - def struct_unpack(self, file_handle, fmt): - num_bytes = struct.calcsize(fmt) - return struct.unpack(fmt, file_handle.read(num_bytes)) - - def init_ngrams(self): - """ - Computes ngrams of all words present in vocabulary and stores vectors for only those ngrams. - Vectors for other ngrams are initialized with a random uniform distribution in FastText. These - vectors are discarded here to save space. - - """ - self.wv.ngrams = {} - all_ngrams = [] - self.wv.syn0 = np.zeros((len(self.wv.vocab), self.vector_size), dtype=REAL) - - for w, vocab in self.wv.vocab.items(): - all_ngrams += compute_ngrams(w, self.wv.min_n, self.wv.max_n) - self.wv.syn0[vocab.index] += np.array(self.wv.syn0_ngrams[vocab.index]) - - all_ngrams = set(all_ngrams) - self.num_ngram_vectors = len(all_ngrams) - ngram_indices = [] - for i, ngram in enumerate(all_ngrams): - ngram_hash = ft_hash(ngram) - ngram_indices.append(len(self.wv.vocab) + ngram_hash % self.bucket) - self.wv.ngrams[ngram] = i - self.wv.syn0_ngrams = self.wv.syn0_ngrams.take(ngram_indices, axis=0) - - ngram_weights = self.wv.syn0_ngrams - - logger.info( - "loading weights for %s words for fastText model from %s", - len(self.wv.vocab), self.file_name - ) - - for w, vocab in self.wv.vocab.items(): - word_ngrams = compute_ngrams(w, self.wv.min_n, self.wv.max_n) - for word_ngram in word_ngrams: - self.wv.syn0[vocab.index] += np.array(ngram_weights[self.wv.ngrams[word_ngram]]) - - self.wv.syn0[vocab.index] /= (len(word_ngrams) + 1) - logger.info( - "loaded %s weight matrix for fastText model from %s", - self.wv.syn0.shape, self.file_name - ) - - -def compute_ngrams(word, min_n, max_n): - BOW, EOW = ('<', '>') # Used by FastText to attach to all words as prefix and suffix - extended_word = BOW + word + EOW - ngrams = [] - for ngram_length in range(min_n, min(len(extended_word), max_n) + 1): - for i in range(0, len(extended_word) - ngram_length + 1): - ngrams.append(extended_word[i:i + ngram_length]) - return ngrams - - -def ft_hash(string): - """ - Reproduces [hash method](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc) - used in fastText. - - """ - # Runtime warnings for integer overflow are raised, this is expected behaviour. These warnings are suppressed. - old_settings = np.seterr(all='ignore') - h = np.uint32(2166136261) - for c in string: - h = h ^ np.uint32(ord(c)) - h = h * np.uint32(16777619) - np.seterr(**old_settings) - return h diff --git a/gensim/models/deprecated/keyedvectors.py b/gensim/models/deprecated/keyedvectors.py deleted file mode 100644 index a8983909d0..0000000000 --- a/gensim/models/deprecated/keyedvectors.py +++ /dev/null @@ -1,1115 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Copyright (C) 2016 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.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: - -.. sourcecode:: pycon - - >>> model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4) - >>> word_vectors = model.wv - -Persist the word vectors to disk with: - -.. sourcecode:: pycon - - >>> 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: - -.. sourcecode:: pycon - - >>> 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: - -.. sourcecode:: pycon - - >>> 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: - -.. sourcecode:: pycon - - >>> word_vectors.evaluate_word_pairs(os.path.join(module_path, 'test_data','wordsim353.tsv')) - 0.51, 0.62, 0.13 - -And on analogies: - -.. sourcecode:: pycon - - >>> word_vectors.accuracy(os.path.join(module_path, 'test_data', '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, ValueError): - PYEMD_EXT = False - -from numpy import dot, zeros, dtype, float32 as REAL,\ - double, array, vstack, fromstring, sqrt, newaxis,\ - ndarray, sum as np_sum, prod, ascontiguousarray,\ - argmax -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, iteritems -from six.moves import range -from scipy import stats - - -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 KeyedVectorsBase(utils.SaveLoad): - """ - Base class to contain vectors and vocab for any set of vectors which are each associated with a key. - - """ - - def __init__(self): - self.syn0 = [] - self.vocab = {} - self.index2word = [] - self.vector_size = None - - 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. - - `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) - - """ - if total_vec is None: - total_vec = len(self.vocab) - vector_size = self.syn0.shape[1] - if fvocab is not None: - logger.info("storing vocabulary in %s", fvocab) - with utils.open(fvocab, 'wb') as vout: - for word, vocab in sorted(iteritems(self.vocab), key=lambda item: -item[1].count): - vout.write(utils.to_utf8("%s %s\n" % (word, vocab.count))) - logger.info("storing %sx%s projection weights into %s", total_vec, vector_size, fname) - assert (len(self.vocab), vector_size) == self.syn0.shape - with utils.open(fname, 'wb') as fout: - fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size))) - # store in sorted order: most frequent words at the top - for word, vocab in sorted(iteritems(self.vocab), key=lambda item: -item[1].count): - row = self.syn0[vocab.index] - if binary: - fout.write(utils.to_utf8(word) + b" " + row.tostring()) - else: - fout.write(utils.to_utf8("%s %s\n" % (word, ' '.join("%f" % val for val in row)))) - - @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. - - 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.) - - """ - counts = None - if fvocab is not None: - logger.info("loading word counts from %s", fvocab) - counts = {} - with utils.open(fvocab, 'rb') as fin: - for line in fin: - word, count = utils.to_unicode(line).strip().split() - counts[word] = int(count) - - logger.info("loading projection weights from %s", fname) - with utils.open(fname, 'rb') as fin: - header = utils.to_unicode(fin.readline(), encoding=encoding) - vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format - if limit: - vocab_size = min(vocab_size, limit) - result = cls() - result.vector_size = vector_size - result.syn0 = zeros((vocab_size, vector_size), dtype=datatype) - - def add_word(word, weights): - word_id = len(result.vocab) - if word in result.vocab: - logger.warning("duplicate word '%s' in %s, ignoring all but first", word, fname) - return - if counts is None: - # most common scenario: no vocab file given. just make up some bogus counts, in descending order - result.vocab[word] = Vocab(index=word_id, count=vocab_size - word_id) - elif word in counts: - # use count from the vocab file - result.vocab[word] = Vocab(index=word_id, count=counts[word]) - else: - # vocab file given, but word is missing -- set count to None (TODO: or raise?) - logger.warning("vocabulary file is incomplete: '%s' is missing", word) - result.vocab[word] = Vocab(index=word_id, count=None) - result.syn0[word_id] = weights - result.index2word.append(word) - - if binary: - binary_len = dtype(REAL).itemsize * vector_size - for _ in range(vocab_size): - # mixed text and binary: read text first, then binary - word = [] - while True: - ch = fin.read(1) - if ch == b' ': - break - if ch == b'': - raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") - if ch != b'\n': # ignore newlines in front of words (some binary files have) - word.append(ch) - word = utils.to_unicode(b''.join(word), encoding=encoding, errors=unicode_errors) - weights = fromstring(fin.read(binary_len), dtype=REAL) - add_word(word, weights) - else: - for line_no in range(vocab_size): - line = fin.readline() - if line == b'': - raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") - parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") - if len(parts) != vector_size + 1: - raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) - word, weights = parts[0], [REAL(x) for x in parts[1:]] - add_word(word, weights) - if result.syn0.shape[0] != len(result.vocab): - logger.info( - "duplicate words detected, shrinking matrix size from %i to %i", - result.syn0.shape[0], len(result.vocab) - ) - result.syn0 = ascontiguousarray(result.syn0[: len(result.vocab)]) - assert (len(result.vocab), vector_size) == result.syn0.shape - - logger.info("loaded %s matrix from %s", result.syn0.shape, fname) - return result - - def similarity(self, w1, w2): - """ - Compute similarity between vectors of two input words. - To be implemented by child class. - - """ - raise NotImplementedError - - def distance(self, w1, w2): - """ - Compute distance between vectors of two input words. - To be implemented by child class. - - """ - raise NotImplementedError - - def distances(self, 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. - - """ - raise NotImplementedError - - def word_vec(self, word): - """ - Accept a single word as input. - Returns the word's representations in vector space, as a 1D numpy array. - - Example: - - .. sourcecode:: pycon - - >>> trained_model.word_vec('office') - array([ -1.40128313e-02, ...]) - - """ - if word in self.vocab: - result = self.syn0[self.vocab[word].index] - result.setflags(write=False) - return result - else: - raise KeyError("word '%s' not in vocabulary" % word) - - def __getitem__(self, words): - """ - Accept a single word or a list of words as input. - - If a single word: returns the word's representations in vector space, as - a 1D numpy array. - - Multiple words: return the words' representations in vector space, as a - 2d numpy array: #words x #vector_size. Matrix rows are in the same order - as in input. - - Example: - - .. sourcecode:: pycon - - >>> trained_model['office'] - array([ -1.40128313e-02, ...]) - - >>> trained_model[['office', 'products']] - array([ -1.40128313e-02, ...] - [ -1.70425311e-03, ...] - ...) - - """ - if isinstance(words, string_types): - # allow calls like trained_model['office'], as a shorthand for trained_model[['office']] - return self.word_vec(words) - - return vstack([self.word_vec(word) for word in words]) - - def __contains__(self, word): - return word in self.vocab - - def most_similar_to_given(self, w1, word_list): - """Return the word from word_list most similar to w1. - - Args: - w1 (str): a word - word_list (list): list of words containing a word most similar to w1 - - Returns: - the word in word_list with the highest similarity to w1 - - Raises: - KeyError: If w1 or any word in word_list is not in the vocabulary - - Example: - - .. sourcecode:: pycon - - >>> trained_model.most_similar_to_given('music', ['water', 'sound', 'backpack', 'mouse']) - 'sound' - - >>> trained_model.most_similar_to_given('snake', ['food', 'pencil', 'animal', 'phone']) - 'animal' - - """ - return word_list[argmax([self.similarity(w1, word) for word in word_list])] - - def words_closer_than(self, w1, w2): - """ - Returns 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`. - - Examples - -------- - - .. sourcecode:: pycon - - >>> model.words_closer_than('carnivore.n.01', 'mammal.n.01') - ['dog.n.01', 'canine.n.02'] - - """ - all_distances = self.distances(w1) - w1_index = self.vocab[w1].index - w2_index = self.vocab[w2].index - closer_node_indices = np.where(all_distances < all_distances[w2_index])[0] - return [self.index2word[index] for index in closer_node_indices if index != w1_index] - - def rank(self, w1, w2): - """ - Rank of the distance of `w2` from `w1`, in relation to distances of all words from `w1`. - - Parameters - ---------- - w1 : str - Input word. - w2 : str - Input word. - - Returns - ------- - int - Rank of `w2` from `w1` in relation to all other nodes. - - Examples - -------- - - .. sourcecode:: pycon - - >>> model.rank('mammal.n.01', 'carnivore.n.01') - 3 - - """ - return len(self.words_closer_than(w1, w2)) + 1 - - -class EuclideanKeyedVectors(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() - """ - - def __init__(self): - super(EuclideanKeyedVectors, self).__init__() - self.syn0norm = None - - @property - def wv(self): - return self - - def save(self, *args, **kwargs): - # don't bother storing the cached normalized vectors - kwargs['ignore'] = kwargs.get('ignore', ['syn0norm']) - super(EuclideanKeyedVectors, self).save(*args, **kwargs) - - def word_vec(self, 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: - - .. sourcecode:: pycon - - >>> trained_model['office'] - array([ -1.40128313e-02, ...]) - - """ - if word in self.vocab: - if use_norm: - result = self.syn0norm[self.vocab[word].index] - else: - result = self.syn0[self.vocab[word].index] - - result.setflags(write=False) - return result - else: - raise KeyError("word '%s' not in vocabulary" % word) - - 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. - - 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: - - .. sourcecode:: pycon - - >>> trained_model.most_similar(positive=['woman', 'king'], negative=['man']) - [('queen', 0.50882536), ...] - - """ - 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.syn0norm if restrict_vocab is None else self.syn0norm[: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. - - 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: - - .. sourcecode:: pycon - - >>> trained_model.similar_by_word('graph') - [('user', 0.9999163150787354), ...] - - """ - 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. - - 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), ...] - - """ - return self.most_similar(positive=[vector], topn=topn, restrict_vocab=restrict_vocab) - - 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". - - 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: - - .. sourcecode:: pycon - - >>> # 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) - """ - - 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 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: - - .. sourcecode:: pycon - - >>> trained_model.most_similar_cosmul(positive=['baghdad', 'england'], negative=['london']) - [(u'iraq', 0.8488819003105164), ...] - - .. [4] Omer Levy and Yoav Goldberg. Linguistic Regularities in Sparse and Explicit Word Representations, 2014. - - """ - 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.syn0norm, term)) / 2) for term in positive] - neg_dists = [((1 + dot(self.syn0norm, 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? - - Example:: - - >>> trained_model.doesnt_match("breakfast cereal dinner lunch".split()) - 'cereal' - - """ - 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): - """ - Return cosine similarities between one vector and a set of other vectors. - - Parameters - ---------- - 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) - - Returns - ------- - numpy.array - 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 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). - - Returns - ------- - numpy.array - Array containing distances to all words in `other_words` from input `word_or_vector`, - in the same order as `other_words`. - - Notes - ----- - 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.syn0 - else: - other_indices = [self.vocab[word].index for word in other_words] - other_vectors = self.syn0[other_indices] - return 1 - self.cosine_similarities(input_vector, other_vectors) - - def distance(self, w1, w2): - """ - Compute cosine distance between two words. - - Example: - - .. sourcecode:: pycon - - >>> trained_model.distance('woman', 'man') - 0.34 - - >>> trained_model.distance('woman', 'woman') - 0.0 - - """ - return 1 - self.similarity(w1, w2) - - def similarity(self, w1, w2): - """ - Compute cosine similarity between two words. - - Example: - - .. sourcecode:: pycon - - >>> trained_model.similarity('woman', 'man') - 0.73723527 - - >>> trained_model.similarity('woman', 'woman') - 1.0 - - """ - return dot(matutils.unitvec(self[w1]), matutils.unitvec(self[w2])) - - def n_similarity(self, ws1, ws2): - """ - Compute cosine similarity between two sets of words. - - Example: - - .. sourcecode:: pycon - - >>> 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 - - """ - 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_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 - ) - - def accuracy(self, questions, restrict_vocab=30000, most_similar=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. - - """ - 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 - with utils.open(questions, 'rb') as f: - for line_no, line in enumerate(f): - # 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. `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. - """ - 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 - - with utils.open(pairs, 'rb') as f: - for line_no, line in enumerate(f): - 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: - 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) - 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. - - 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`. - - """ - if getattr(self, 'syn0norm', None) is None or replace: - logger.info("precomputing L2-norms of word weight vectors") - if replace: - for i in range(self.syn0.shape[0]): - self.syn0[i, :] /= sqrt((self.syn0[i, :] ** 2).sum(-1)) - self.syn0norm = self.syn0 - else: - self.syn0norm = (self.syn0 / sqrt((self.syn0 ** 2).sum(-1))[..., newaxis]).astype(REAL) - - def get_keras_embedding(self, train_embeddings=False): - """ - Return a Keras 'Embedding' layer with weights set as the Word2Vec model's learned word embeddings - """ - try: - from keras.layers import Embedding - except ImportError: - raise ImportError("Please install Keras to use this function") - weights = self.syn0 - - # 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 - - -# For backward compatibility -KeyedVectors = EuclideanKeyedVectors diff --git a/gensim/models/deprecated/old_saveload.py b/gensim/models/deprecated/old_saveload.py deleted file mode 100644 index 750d83ed44..0000000000 --- a/gensim/models/deprecated/old_saveload.py +++ /dev/null @@ -1,398 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Copyright (C) 2018 Radim Rehurek -# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html -""" -Warnings --------- -.. deprecated:: 3.3.0 - Use :mod:`gensim.utils` instead. - - -Class containing the old SaveLoad class with modeified `unpickle` function is support loading models saved using -an older gensim version. - -""" -from __future__ import with_statement - -import logging - -try: - import cPickle as _pickle -except ImportError: - import pickle as _pickle - -import re -import sys - -import numpy as np -import scipy.sparse - -from six import iteritems - -from gensim import utils - -if sys.version_info[0] >= 3: - unicode = str - -logger = logging.getLogger(__name__) - - -PAT_ALPHABETIC = re.compile(r'(((?![\d])\w)+)', re.UNICODE) -RE_HTML_ENTITY = re.compile(r'&(#?)([xX]?)(\w{1,8});', re.UNICODE) - - -class SaveLoad(object): - """Class which inherit from this class have save/load functions, which un/pickle them to disk. - - Warnings - -------- - This uses pickle for de/serializing, so objects must not contain unpicklable attributes, - such as lambda functions etc. - - """ - @classmethod - def load(cls, fname, mmap=None): - """Load a previously saved object (using :meth:`~gensim.utils.SaveLoad.save`) from file. - - Parameters - ---------- - 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 - -------- - :meth:`~gensim.utils.SaveLoad.save` - - Returns - ------- - object - Object loaded from `fname`. - - Raises - ------ - IOError - When methods are called on instance (should be called from class). - - """ - logger.info("loading %s object from %s", cls.__name__, fname) - - compress, subname = SaveLoad._adapt_by_suffix(fname) - - obj = unpickle(fname) - obj._load_specials(fname, mmap, compress, subname) - logger.info("loaded %s", fname) - return obj - - def _load_specials(self, fname, mmap, compress, subname): - """Loads any attributes that were stored specially, and gives the same opportunity - to recursively included :class:`~gensim.utils.SaveLoad` instances. - - Parameters - ---------- - fname : str - Path to file that contains needed object. - mmap : str - Memory-map option. - compress : bool - Set to True if file is compressed. - subname : str - ... - - - """ - def mmap_error(obj, filename): - return IOError( - 'Cannot mmap compressed object %s in file %s. ' % (obj, filename) - + 'Use `load(fname, mmap=None)` or uncompress files manually.' - ) - - for attrib in getattr(self, '__recursive_saveloads', []): - cfname = '.'.join((fname, attrib)) - logger.info("loading %s recursively from %s.* with mmap=%s", attrib, cfname, mmap) - getattr(self, attrib)._load_specials(cfname, mmap, compress, subname) - - for attrib in getattr(self, '__numpys', []): - logger.info("loading %s from %s with mmap=%s", attrib, subname(fname, attrib), mmap) - - if compress: - if mmap: - raise mmap_error(attrib, subname(fname, attrib)) - - val = np.load(subname(fname, attrib))['val'] - else: - val = np.load(subname(fname, attrib), mmap_mode=mmap) - - setattr(self, attrib, val) - - for attrib in getattr(self, '__scipys', []): - logger.info("loading %s from %s with mmap=%s", attrib, subname(fname, attrib), mmap) - sparse = unpickle(subname(fname, attrib)) - if compress: - if mmap: - raise mmap_error(attrib, subname(fname, attrib)) - - with np.load(subname(fname, attrib, 'sparse')) as f: - sparse.data = f['data'] - sparse.indptr = f['indptr'] - sparse.indices = f['indices'] - else: - sparse.data = np.load(subname(fname, attrib, 'data'), mmap_mode=mmap) - sparse.indptr = np.load(subname(fname, attrib, 'indptr'), mmap_mode=mmap) - sparse.indices = np.load(subname(fname, attrib, 'indices'), mmap_mode=mmap) - - setattr(self, attrib, sparse) - - for attrib in getattr(self, '__ignoreds', []): - logger.info("setting ignored attribute %s to None", attrib) - setattr(self, attrib, None) - - @staticmethod - def _adapt_by_suffix(fname): - """Give appropriate compress setting and filename formula. - - Parameters - ---------- - fname : str - Input filename. - - Returns - ------- - (bool, function) - First argument will be True if `fname` compressed. - - """ - compress, suffix = (True, 'npz') if fname.endswith('.gz') or fname.endswith('.bz2') else (False, 'npy') - return compress, lambda *args: '.'.join(args + (suffix,)) - - def _smart_save(self, fname, separately=None, sep_limit=10 * 1024**2, ignore=frozenset(), pickle_protocol=2): - """Save the object to file. - - Parameters - ---------- - fname : str - Path to file. - separately : list, optional - Iterable of attributes than need to store distinctly. - sep_limit : int, optional - Limit for separation. - ignore : frozenset, optional - Attributes that shouldn't be store. - pickle_protocol : int, optional - Protocol number for pickle. - - Notes - ----- - If `separately` is None, automatically detect large - numpy/scipy.sparse arrays in the object being stored, and store - them into separate files. This avoids pickle memory errors and - allows mmap'ing large arrays back on load efficiently. - - You can also set `separately` manually, in which case it must be - a list of attribute names to be stored in separate files. The - automatic check is not performed in this case. - - See Also - -------- - :meth:`~gensim.utils.SaveLoad.load` - - """ - logger.info("saving %s object under %s, separately %s", self.__class__.__name__, fname, separately) - - compress, subname = SaveLoad._adapt_by_suffix(fname) - - restores = self._save_specials(fname, separately, sep_limit, ignore, pickle_protocol, - compress, subname) - try: - pickle(self, fname, protocol=pickle_protocol) - finally: - # restore attribs handled specially - for obj, asides in restores: - for attrib, val in iteritems(asides): - setattr(obj, attrib, val) - logger.info("saved %s", fname) - - def _save_specials(self, fname, separately, sep_limit, ignore, pickle_protocol, compress, subname): - """Save aside any attributes that need to be handled separately, including - by recursion any attributes that are themselves :class:`~gensim.utils.SaveLoad` instances. - - Parameters - ---------- - fname : str - Output filename. - separately : list or None - Iterable of attributes than need to store distinctly - sep_limit : int - Limit for separation. - ignore : iterable of str - Attributes that shouldn't be store. - pickle_protocol : int - Protocol number for pickle. - compress : bool - If True - compress output with :func:`numpy.savez_compressed`. - subname : function - Produced by :meth:`~gensim.utils.SaveLoad._adapt_by_suffix` - - Returns - ------- - list of (obj, {attrib: value, ...}) - Settings that the caller should use to restore each object's attributes that were set aside - during the default :func:`~gensim.utils.pickle`. - - """ - asides = {} - sparse_matrices = (scipy.sparse.csr_matrix, scipy.sparse.csc_matrix) - if separately is None: - separately = [] - for attrib, val in iteritems(self.__dict__): - if isinstance(val, np.ndarray) and val.size >= sep_limit: - separately.append(attrib) - elif isinstance(val, sparse_matrices) and val.nnz >= sep_limit: - separately.append(attrib) - - # whatever's in `separately` or `ignore` at this point won't get pickled - for attrib in separately + list(ignore): - if hasattr(self, attrib): - asides[attrib] = getattr(self, attrib) - delattr(self, attrib) - - recursive_saveloads = [] - restores = [] - for attrib, val in iteritems(self.__dict__): - if hasattr(val, '_save_specials'): # better than 'isinstance(val, SaveLoad)' if IPython reloading - recursive_saveloads.append(attrib) - cfname = '.'.join((fname, attrib)) - restores.extend(val._save_specials(cfname, None, sep_limit, ignore, pickle_protocol, compress, subname)) - - try: - numpys, scipys, ignoreds = [], [], [] - for attrib, val in iteritems(asides): - if isinstance(val, np.ndarray) and attrib not in ignore: - numpys.append(attrib) - logger.info("storing np array '%s' to %s", attrib, subname(fname, attrib)) - - if compress: - np.savez_compressed(subname(fname, attrib), val=np.ascontiguousarray(val)) - else: - np.save(subname(fname, attrib), np.ascontiguousarray(val)) - - elif isinstance(val, (scipy.sparse.csr_matrix, scipy.sparse.csc_matrix)) and attrib not in ignore: - scipys.append(attrib) - logger.info("storing scipy.sparse array '%s' under %s", attrib, subname(fname, attrib)) - - if compress: - np.savez_compressed( - subname(fname, attrib, 'sparse'), - data=val.data, - indptr=val.indptr, - indices=val.indices - ) - else: - np.save(subname(fname, attrib, 'data'), val.data) - np.save(subname(fname, attrib, 'indptr'), val.indptr) - np.save(subname(fname, attrib, 'indices'), val.indices) - - data, indptr, indices = val.data, val.indptr, val.indices - val.data, val.indptr, val.indices = None, None, None - - try: - # store array-less object - pickle(val, subname(fname, attrib), protocol=pickle_protocol) - finally: - val.data, val.indptr, val.indices = data, indptr, indices - else: - logger.info("not storing attribute %s", attrib) - ignoreds.append(attrib) - - self.__dict__['__numpys'] = numpys - self.__dict__['__scipys'] = scipys - self.__dict__['__ignoreds'] = ignoreds - self.__dict__['__recursive_saveloads'] = recursive_saveloads - except Exception: - # restore the attributes if exception-interrupted - for attrib, val in iteritems(asides): - setattr(self, attrib, val) - raise - return restores + [(self, asides)] - - def save(self, fname_or_handle, separately=None, sep_limit=10 * 1024**2, ignore=frozenset(), pickle_protocol=2): - """Save the object to file. - - Parameters - ---------- - 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 avoids pickle memory errors and allows mmap'ing large arrays - back on load efficiently. - If list of str - this attributes will be stored in separate files, the automatic check - is not performed in this case. - sep_limit : int - Limit for automatic separation. - ignore : frozenset of str - Attributes that shouldn't be serialize/store. - pickle_protocol : int - Protocol number for pickle. - - See Also - -------- - :meth:`~gensim.utils.SaveLoad.load` - - """ - try: - _pickle.dump(self, fname_or_handle, protocol=pickle_protocol) - logger.info("saved %s object", self.__class__.__name__) - except TypeError: # `fname_or_handle` does not have write attribute - self._smart_save(fname_or_handle, separately, sep_limit, ignore, pickle_protocol=pickle_protocol) - - -def unpickle(fname): - """Load object from `fname`. - - Parameters - ---------- - fname : str - Path to pickle file. - - Returns - ------- - object - Python object loaded from `fname`. - - """ - with utils.open(fname, 'rb') as f: - file_bytes = f.read() - file_bytes = file_bytes.replace(b'gensim.models.word2vec', b'gensim.models.deprecated.word2vec') - file_bytes = file_bytes.replace(b'gensim.models.keyedvectors', b'gensim.models.deprecated.keyedvectors') - file_bytes = file_bytes.replace(b'gensim.models.doc2vec', b'gensim.models.deprecated.doc2vec') - file_bytes = file_bytes.replace(b'gensim.models.fasttext', b'gensim.models.deprecated.fasttext') - file_bytes = file_bytes.replace( - b'gensim.models.wrappers.fasttext', b'gensim.models.deprecated.fasttext_wrapper') - if sys.version_info > (3, 0): - return _pickle.loads(file_bytes, encoding='latin1') - else: - return _pickle.loads(file_bytes) - - -def pickle(obj, fname, protocol=2): - """Pickle object `obj` to file `fname`. - - Parameters - ---------- - obj : object - Any python object. - fname : str - Path to pickle file. - protocol : int, optional - Pickle protocol number, default is 2 to support compatible across python 2.x and 3.x. - - """ - with utils.open(fname, 'wb') as fout: # 'b' for binary, needed on Windows - _pickle.dump(obj, fout, protocol=protocol) diff --git a/gensim/models/deprecated/word2vec.py b/gensim/models/deprecated/word2vec.py deleted file mode 100644 index d57a902c55..0000000000 --- a/gensim/models/deprecated/word2vec.py +++ /dev/null @@ -1,1907 +0,0 @@ -#!/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.word2vec` instead. - - -Produce word vectors with deep learning via word2vec's "skip-gram and CBOW models", using either -hierarchical softmax or negative sampling [1]_ [2]_. - -NOTE: There are more ways to get word vectors in Gensim than just Word2Vec. -See wrappers for FastText, VarEmbed and WordRank. - -The training algorithms were originally ported from the C package https://code.google.com/p/word2vec/ -and extended with additional functionality. - -For a blog tutorial on gensim word2vec, with an interactive web app trained on GoogleNews, -visit http://radimrehurek.com/2014/02/word2vec-tutorial/ - -**Make sure you have a C compiler before installing gensim, to use optimized (compiled) word2vec training** -(70x speedup compared to plain NumPy implementation [3]_). - -Initialize a model with e.g.: - -.. sourcecode:: pycon - - >>> model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4) - -Persist a model to disk with: - -.. sourcecode:: pycon - - >>> model.save(fname) - >>> model = Word2Vec.load(fname) # you can continue training with the loaded model! - -The word vectors are stored in a KeyedVectors instance in model.wv. -This separates the read-only word vector lookup operations in KeyedVectors from the training code in Word2Vec: - -.. sourcecode:: pycon - - >>> model.wv['computer'] # numpy vector of a word - array([-0.00449447, -0.00310097, 0.02421786, ...], dtype=float32) - -The word vectors can also be instantiated from an existing file on disk in the word2vec C format -as a KeyedVectors instance:: - - NOTE: It is impossible to continue training the vectors loaded from the C format because hidden weights, - vocabulary frequency and the binary tree is missing: - - .. sourcecode:: pycon - - >>> 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 NLP word tasks with the model. Some of them -are already built-in: - -.. sourcecode:: pycon - - >>> model.wv.most_similar(positive=['woman', 'king'], negative=['man']) - [('queen', 0.50882536), ...] - - >>> model.wv.most_similar_cosmul(positive=['woman', 'king'], negative=['man']) - [('queen', 0.71382287), ...] - - >>> model.wv.doesnt_match("breakfast cereal dinner lunch".split()) - 'cereal' - - >>> model.wv.similarity('woman', 'man') - 0.73723527 - -Probability of a text under the model: - -.. sourcecode:: pycon - - >>> model.score(["The fox jumped over a lazy dog".split()]) - 0.2158356 - -Correlation with human opinion on word similarity: - -.. sourcecode:: pycon - - >>> model.wv.evaluate_word_pairs(os.path.join(module_path, 'test_data','wordsim353.tsv')) - 0.51, 0.62, 0.13 - -And on analogies: - -.. sourcecode:: pycon - - >>> model.wv.accuracy(os.path.join(module_path, 'test_data', 'questions-words.txt')) - -and so on. - -If you're finished training a model (i.e. no more updates, only querying), -then switch to the :mod:`gensim.models.KeyedVectors` instance in wv - -.. sourcecode:: pycon - - >>> word_vectors = model.wv - >>> del model - -to trim unneeded model memory = use much less RAM. - -Note that there is a :mod:`gensim.models.phrases` module which lets you automatically -detect phrases longer than one word. Using phrases, you can learn a word2vec model -where "words" are actually multiword expressions, such as `new_york_times` or `financial_crisis`: - -.. sourcecode:: pycon - - >>> bigram_transformer = gensim.models.Phrases(sentences) - >>> model = Word2Vec(bigram_transformer[sentences], size=100, ...) - -.. [1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. - Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013. -.. [2] 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. -.. [3] Optimizing word2vec in gensim, http://radimrehurek.com/2013/09/word2vec-in-python-part-two-optimizing/ -""" -from __future__ import division # py3 "true division" - -import logging -import sys -import os -import heapq -from timeit import default_timer -from copy import deepcopy -from collections import defaultdict -import threading -import itertools -import warnings - -from gensim.utils import keep_vocab_item, call_on_class_only -from gensim.models.deprecated.keyedvectors import KeyedVectors, Vocab -from gensim.models.word2vec import Word2Vec as NewWord2Vec -from gensim.models.deprecated.old_saveload import SaveLoad - -try: - from queue import Queue, Empty -except ImportError: - from Queue import Queue, Empty - -from numpy import exp, log, dot, zeros, outer, random, dtype, float32 as REAL,\ - uint32, seterr, array, uint8, vstack, fromstring, sqrt,\ - empty, sum as np_sum, ones, logaddexp - -from scipy.special import expit - -from gensim import utils -from gensim import matutils # utility fnc for pickling, common scipy operations etc -from six import iteritems, itervalues, string_types -from six.moves import range -from types import GeneratorType - -logger = logging.getLogger(__name__) - -MAX_WORDS_IN_BATCH = 10000 - - -def load_old_word2vec(*args, **kwargs): - old_model = Word2Vec.load(*args, **kwargs) - vector_size = getattr(old_model, 'vector_size', old_model.layer1_size) - params = { - 'size': 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.__dict__.get('sample', 1e-3), - 'seed': old_model.seed, - 'workers': old_model.workers, - 'min_alpha': old_model.min_alpha, - 'sg': old_model.sg, - 'hs': old_model.hs, - 'negative': old_model.negative, - 'cbow_mean': old_model.cbow_mean, - 'hashfxn': old_model.__dict__.get('hashfxn', hash), - 'iter': old_model.__dict__.get('iter', 5), - 'null_word': old_model.__dict__.get('null_word', 0), - '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 = NewWord2Vec(**params) - # set trainables attributes - new_model.wv.vectors = old_model.wv.syn0 - if hasattr(old_model.wv, 'syn0norm'): - new_model.wv.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 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.__dict__.get('cum_table', None) - - new_model.train_count = old_model.__dict__.get('train_count', None) - new_model.corpus_count = old_model.__dict__.get('corpus_count', None) - new_model.corpus_total_words = old_model.__dict__.get('corpus_total_words', 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_batch_sg(model, sentences, alpha, work=None, compute_loss=False): - """ - Update skip-gram model by training on a sequence of sentences. - - Each sentence is a list of string tokens, which are looked up in the model's - vocab dictionary. Called internally from `Word2Vec.train()`. - - This is the non-optimized, Python version. If you have cython installed, gensim - will use the optimized version from word2vec_inner instead. - - """ - result = 0 - for sentence in sentences: - word_vocabs = [model.wv.vocab[w] for w in sentence 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 word2vec code - - # now go over all words from the (reduced) window, predicting each one in turn - start = max(0, pos - model.window + reduced_window) - for pos2, word2 in enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start): - # don't train on the `word` itself - if pos2 != pos: - train_sg_pair( - model, model.wv.index2word[word.index], word2.index, alpha, compute_loss=compute_loss - ) - - result += len(word_vocabs) - return result - - -def train_batch_cbow(model, sentences, alpha, work=None, neu1=None, compute_loss=False): - """ - Update CBOW model by training on a sequence of sentences. - - Each sentence is a list of string tokens, which are looked up in the model's - vocab dictionary. Called internally from `Word2Vec.train()`. - - This is the non-optimized, Python version. If you have cython installed, gensim - will use the optimized version from word2vec_inner instead. - - """ - result = 0 - for sentence in sentences: - word_vocabs = [model.wv.vocab[w] for w in sentence 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 word2vec code - start = max(0, pos - model.window + reduced_window) - window_pos = enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start) - word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)] - l1 = np_sum(model.wv.syn0[word2_indices], axis=0) # 1 x vector_size - if word2_indices and model.cbow_mean: - l1 /= len(word2_indices) - train_cbow_pair(model, word, word2_indices, l1, alpha, compute_loss=compute_loss) - result += len(word_vocabs) - return result - - -def score_sentence_sg(model, sentence, work=None): - """ - Obtain likelihood score for a single sentence in a fitted skip-gram representaion. - - The sentence is a list of Vocab objects (or None, when the corresponding - word is not in the vocabulary). Called internally from `Word2Vec.score()`. - - This is the non-optimized, Python version. If you have cython installed, gensim - will use the optimized version from word2vec_inner instead. - - """ - log_prob_sentence = 0.0 - if model.negative: - raise RuntimeError("scoring is only available for HS=True") - - word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab] - for pos, word in enumerate(word_vocabs): - if word is None: - continue # OOV word in the input sentence => skip - - # now go over all words from the window, predicting each one in turn - start = max(0, pos - model.window) - for pos2, word2 in enumerate(word_vocabs[start: pos + model.window + 1], start): - # don't train on OOV words and on the `word` itself - if word2 is not None and pos2 != pos: - log_prob_sentence += score_sg_pair(model, word, word2) - - return log_prob_sentence - - -def score_sentence_cbow(model, sentence, work=None, neu1=None): - """ - Obtain likelihood score for a single sentence in a fitted CBOW representaion. - - The sentence is a list of Vocab objects (or None, where the corresponding - word is not in the vocabulary. Called internally from `Word2Vec.score()`. - - This is the non-optimized, Python version. If you have cython installed, gensim - will use the optimized version from word2vec_inner instead. - - """ - log_prob_sentence = 0.0 - if model.negative: - raise RuntimeError("scoring is only available for HS=True") - - word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab] - for pos, word in enumerate(word_vocabs): - if word is None: - continue # OOV word in the input sentence => skip - - start = max(0, pos - model.window) - window_pos = enumerate(word_vocabs[start:(pos + model.window + 1)], start) - word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)] - l1 = np_sum(model.wv.syn0[word2_indices], axis=0) # 1 x layer1_size - if word2_indices and model.cbow_mean: - l1 /= len(word2_indices) - log_prob_sentence += score_cbow_pair(model, word, l1) - - return log_prob_sentence - - -def train_sg_pair(model, word, context_index, alpha, learn_vectors=True, learn_hidden=True, - context_vectors=None, context_locks=None, compute_loss=False, is_ft=False): - if context_vectors is None: - if is_ft: - context_vectors_vocab = model.wv.syn0_vocab - context_vectors_ngrams = model.wv.syn0_ngrams - else: - context_vectors = model.wv.syn0 - if context_locks is None: - if is_ft: - context_locks_vocab = model.syn0_vocab_lockf - context_locks_ngrams = model.syn0_ngrams_lockf - else: - context_locks = model.syn0_lockf - - if word not in model.wv.vocab: - return - predict_word = model.wv.vocab[word] # target word (NN output) - - if is_ft: - l1_vocab = context_vectors_vocab[context_index[0]] - l1_ngrams = np_sum(context_vectors_ngrams[context_index[1:]], axis=0) - if context_index: - l1 = np_sum([l1_vocab, l1_ngrams], axis=0) / len(context_index) - else: - l1 = context_vectors[context_index] # input word (NN input/projection layer) - lock_factor = context_locks[context_index] - - neu1e = zeros(l1.shape) - - if model.hs: - # work on the entire tree at once, to push as much work into numpy's C routines as possible (performance) - l2a = deepcopy(model.syn1[predict_word.point]) # 2d matrix, codelen x layer1_size - prod_term = dot(l1, l2a.T) - fa = expit(prod_term) # propagate hidden -> output - ga = (1 - predict_word.code - fa) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1[predict_word.point] += outer(ga, l1) # learn hidden -> output - neu1e += dot(ga, l2a) # save error - - # loss component corresponding to hierarchical softmax - if compute_loss: - sgn = (-1.0)**predict_word.code # `ch` function, 0 -> 1, 1 -> -1 - lprob = -log(expit(-sgn * prod_term)) - model.running_training_loss += sum(lprob) - - if model.negative: - # use this word (label = 1) + `negative` other random words not from this sentence (label = 0) - word_indices = [predict_word.index] - while len(word_indices) < model.negative + 1: - w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1])) - if w != predict_word.index: - word_indices.append(w) - l2b = model.syn1neg[word_indices] # 2d matrix, k+1 x layer1_size - prod_term = dot(l1, l2b.T) - fb = expit(prod_term) # propagate hidden -> output - gb = (model.neg_labels - fb) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1neg[word_indices] += outer(gb, l1) # learn hidden -> output - neu1e += dot(gb, l2b) # save error - - # loss component corresponding to negative sampling - if compute_loss: - model.running_training_loss -= sum(log(expit(-1 * prod_term[1:]))) # for the sampled words - model.running_training_loss -= log(expit(prod_term[0])) # for the output word - - if learn_vectors: - if is_ft: - model.wv.syn0_vocab[context_index[0]] += neu1e * context_locks_vocab[context_index[0]] - for i in context_index[1:]: - model.wv.syn0_ngrams[i] += neu1e * context_locks_ngrams[i] - else: - l1 += neu1e * lock_factor # learn input -> hidden (mutates model.wv.syn0[word2.index], if that is l1) - return neu1e - - -def train_cbow_pair(model, word, input_word_indices, l1, alpha, learn_vectors=True, learn_hidden=True, - compute_loss=False, context_vectors=None, context_locks=None, is_ft=False): - if context_vectors is None: - if is_ft: - context_vectors_vocab = model.wv.syn0_vocab - context_vectors_ngrams = model.wv.syn0_ngrams - else: - context_vectors = model.wv.syn0 - if context_locks is None: - if is_ft: - context_locks_vocab = model.syn0_vocab_lockf - context_locks_ngrams = model.syn0_ngrams_lockf - else: - context_locks = model.syn0_lockf - - neu1e = zeros(l1.shape) - - if model.hs: - l2a = model.syn1[word.point] # 2d matrix, codelen x layer1_size - prod_term = dot(l1, l2a.T) - fa = expit(prod_term) # propagate hidden -> output - ga = (1. - word.code - fa) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1[word.point] += outer(ga, l1) # learn hidden -> output - neu1e += dot(ga, l2a) # save error - - # loss component corresponding to hierarchical softmax - if compute_loss: - sgn = (-1.0)**word.code # ch function, 0-> 1, 1 -> -1 - model.running_training_loss += sum(-log(expit(-sgn * prod_term))) - - if model.negative: - # use this word (label = 1) + `negative` other random words not from this sentence (label = 0) - word_indices = [word.index] - while len(word_indices) < model.negative + 1: - w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1])) - if w != word.index: - word_indices.append(w) - l2b = model.syn1neg[word_indices] # 2d matrix, k+1 x layer1_size - prod_term = dot(l1, l2b.T) - fb = expit(prod_term) # propagate hidden -> output - gb = (model.neg_labels - fb) * alpha # vector of error gradients multiplied by the learning rate - if learn_hidden: - model.syn1neg[word_indices] += outer(gb, l1) # learn hidden -> output - neu1e += dot(gb, l2b) # save error - - # loss component corresponding to negative sampling - if compute_loss: - model.running_training_loss -= sum(log(expit(-1 * prod_term[1:]))) # for the sampled words - model.running_training_loss -= log(expit(prod_term[0])) # for the output word - - if learn_vectors: - # learn input -> hidden, here for all words in the window separately - if is_ft: - if not model.cbow_mean and input_word_indices: - neu1e /= (len(input_word_indices[0]) + len(input_word_indices[1])) - for i in input_word_indices[0]: - context_vectors_vocab[i] += neu1e * context_locks_vocab[i] - for i in input_word_indices[1]: - context_vectors_ngrams[i] += neu1e * context_locks_ngrams[i] - else: - if not model.cbow_mean and input_word_indices: - neu1e /= len(input_word_indices) - for i in input_word_indices: - context_vectors[i] += neu1e * context_locks[i] - - return neu1e - - -def score_sg_pair(model, word, word2): - l1 = model.wv.syn0[word2.index] - l2a = deepcopy(model.syn1[word.point]) # 2d matrix, codelen x layer1_size - sgn = (-1.0)**word.code # ch function, 0-> 1, 1 -> -1 - lprob = -logaddexp(0, -sgn * dot(l1, l2a.T)) - return sum(lprob) - - -def score_cbow_pair(model, word, l1): - l2a = model.syn1[word.point] # 2d matrix, codelen x layer1_size - sgn = (-1.0)**word.code # ch function, 0-> 1, 1 -> -1 - lprob = -logaddexp(0, -sgn * dot(l1, l2a.T)) - return sum(lprob) - - -class Word2Vec(SaveLoad): - """ - Class for training, using and evaluating neural networks described in https://code.google.com/p/word2vec/ - - If you're finished training a model (=no more updates, only querying) - then switch to the :mod:`gensim.models.KeyedVectors` instance in wv - - The model can be stored/loaded via its `save()` and `load()` methods, or stored/loaded in a format - compatible with the original word2vec implementation via `wv.save_word2vec_format()` - and `KeyedVectors.load_word2vec_format()`. - - """ - - def __init__(self, sentences=None, size=100, alpha=0.025, window=5, min_count=5, - max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, - sg=0, hs=0, negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, - trim_rule=None, sorted_vocab=1, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False): - """ - Initialize the model from an iterable of `sentences`. Each sentence is a - list of words (unicode strings) that will be used for training. - - The `sentences` iterable can be simply a list, but for larger corpora, - consider an iterable that streams the sentences directly from disk/network. - See :class:`BrownCorpus`, :class:`Text8Corpus` or :class:`LineSentence` in - this module for such examples. - - If you don't supply `sentences`, the model is left uninitialized -- use if - you plan to initialize it in some other way. - - `sg` defines the training algorithm. By default (`sg=0`), CBOW is used. - Otherwise (`sg=1`), skip-gram is employed. - - `size` is the dimensionality of the feature vectors. - - `window` is the maximum distance between the current and predicted word within a sentence. - - `alpha` is the initial learning rate (will linearly drop to `min_alpha` as training progresses). - - `seed` = for the random number generator. Initial vectors for each - word are seeded with a hash of the concatenation of word + str(seed). - 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, useful range is (0, 1e-5). - - `workers` = use this many worker threads to train the model (=faster training with multicore machines). - - `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. - - `cbow_mean` = if 0, use the sum of the context word vectors. If 1 (default), use the mean. - Only applies when cbow is used. - - `hashfxn` = hash function to use to randomly initialize weights, for increased - training reproducibility. Default is Python's rudimentary built in hash function. - - `iter` = number of iterations (epochs) over the corpus. Default is 5. - - `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 `utils.RULE_DISCARD`, `utils.RULE_KEEP` or `utils.RULE_DEFAULT`. - Note: The rule, if given, is only used to prune vocabulary during build_vocab() and is not stored as part - of the model. - - `sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before - assigning word indexes. - - `batch_words` = target size (in words) for batches of examples passed to worker threads (and - thus cython routines). Default is 10000. (Larger batches will be passed if individual - texts are longer than 10000 words, but the standard cython code truncates to that maximum.) - - """ - - self.load = call_on_class_only - - self.initialize_word_vectors() - self.sg = int(sg) - self.cum_table = None # for negative sampling - self.vector_size = int(size) - self.layer1_size = int(size) - if size % 4 != 0: - logger.warning("consider setting layer size to a multiple of 4 for greater performance") - self.alpha = float(alpha) - self.min_alpha_yet_reached = float(alpha) # To warn user if alpha increases - self.window = int(window) - self.max_vocab_size = max_vocab_size - self.seed = seed - self.random = random.RandomState(seed) - self.min_count = min_count - self.sample = sample - self.workers = int(workers) - self.min_alpha = float(min_alpha) - self.hs = hs - self.negative = negative - self.cbow_mean = int(cbow_mean) - self.hashfxn = hashfxn - self.iter = iter - self.null_word = null_word - self.train_count = 0 - self.total_train_time = 0 - self.sorted_vocab = sorted_vocab - self.batch_words = batch_words - self.model_trimmed_post_training = False - self.compute_loss = compute_loss - self.running_training_loss = 0 - if sentences is not None: - if isinstance(sentences, GeneratorType): - raise TypeError("You can't pass a generator as the sentences argument. Try an iterator.") - self.build_vocab(sentences, trim_rule=trim_rule) - self.train( - sentences, total_examples=self.corpus_count, epochs=self.iter, - start_alpha=self.alpha, end_alpha=self.min_alpha - ) - else: - if trim_rule is not None: - logger.warning( - "The rule, if given, is only used to prune vocabulary during build_vocab() " - "and is not stored as part of the model. Model initialized without sentences. " - "trim_rule provided, if any, will be ignored." - ) - - def initialize_word_vectors(self): - self.wv = KeyedVectors() - - def make_cum_table(self, power=0.75, domain=2**31 - 1): - """ - Create a cumulative-distribution table using stored vocabulary word counts for - drawing random words in the negative-sampling training routines. - - To draw a word index, choose a random integer up to the maximum value in the - table (cum_table[-1]), then finding that integer's sorted insertion point - (as if by bisect_left or ndarray.searchsorted()). That insertion point is the - drawn index, coming up in proportion equal to the increment at that slot. - - Called internally from 'build_vocab()'. - """ - vocab_size = len(self.wv.index2word) - self.cum_table = zeros(vocab_size, dtype=uint32) - # compute sum of all power (Z in paper) - train_words_pow = 0.0 - for word_index in range(vocab_size): - train_words_pow += self.wv.vocab[self.wv.index2word[word_index]].count**power - cumulative = 0.0 - for word_index in range(vocab_size): - cumulative += self.wv.vocab[self.wv.index2word[word_index]].count**power - self.cum_table[word_index] = round(cumulative / train_words_pow * domain) - if len(self.cum_table) > 0: - assert self.cum_table[-1] == domain - - def create_binary_tree(self): - """ - Create a binary Huffman tree using stored vocabulary word counts. Frequent words - will have shorter binary codes. Called internally from `build_vocab()`. - - """ - logger.info("constructing a huffman tree from %i words", len(self.wv.vocab)) - - # build the huffman tree - heap = list(itervalues(self.wv.vocab)) - heapq.heapify(heap) - for i in range(len(self.wv.vocab) - 1): - min1, min2 = heapq.heappop(heap), heapq.heappop(heap) - heapq.heappush( - heap, Vocab(count=min1.count + min2.count, index=i + len(self.wv.vocab), left=min1, right=min2) - ) - - # recurse over the tree, assigning a binary code to each vocabulary word - if heap: - max_depth, stack = 0, [(heap[0], [], [])] - while stack: - node, codes, points = stack.pop() - if node.index < len(self.wv.vocab): - # leaf node => store its path from the root - node.code, node.point = codes, points - max_depth = max(len(codes), max_depth) - else: - # inner node => continue recursion - points = array(list(points) + [node.index - len(self.wv.vocab)], dtype=uint32) - stack.append((node.left, array(list(codes) + [0], dtype=uint8), points)) - stack.append((node.right, array(list(codes) + [1], dtype=uint8), points)) - - logger.info("built huffman tree with maximum node depth %i", max_depth) - - def build_vocab(self, sentences, keep_raw_vocab=False, trim_rule=None, progress_per=10000, update=False): - """ - Build vocabulary from a sequence of sentences (can be a once-only generator stream). - Each sentence must be a list of unicode strings. - """ - self.scan_vocab(sentences, progress_per=progress_per, trim_rule=trim_rule) # initial survey - # trim by min_count & precalculate downsampling - self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update) - self.finalize_vocab(update=update) # build tables & arrays - - def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=None, trim_rule=None, update=False): - """ - Build vocabulary from a dictionary of word frequencies. - Build model vocabulary from a passed dictionary that contains (word,word count). - Words must be of type unicode strings. - - Parameters - ---------- - `word_freq` : dict - Word,Word_Count dictionary. - `keep_raw_vocab` : bool - If not true, delete the raw vocabulary after the scaling is done and free up RAM. - `corpus_count`: int - Even if no corpus is provided, this argument can set corpus_count explicitly. - `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 `utils.RULE_DISCARD`, `utils.RULE_KEEP` or `utils.RULE_DEFAULT`. - `update`: bool - If true, the new provided words in `word_freq` dict will be added to model's vocab. - - Returns - -------- - None - - Examples - -------- - - .. sourcecode:: pycon - - >>> from gensim.models.word2vec import Word2Vec - >>> model = Word2Vec() - >>> model.build_vocab_from_freq({"Word1": 15, "Word2": 20}) - - """ - logger.info("Processing provided word frequencies") - # Instead of scanning text, this will assign provided word frequencies dictionary(word_freq) - # to be directly the raw vocab - raw_vocab = word_freq - logger.info( - "collected %i different raw word, with total frequency of %i", - len(raw_vocab), sum(itervalues(raw_vocab)) - ) - - # Since no sentences are provided, this is to control the corpus_count - self.corpus_count = corpus_count if corpus_count else 0 - self.raw_vocab = raw_vocab - - # trim by min_count & precalculate downsampling - self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update) - self.finalize_vocab(update=update) # build tables & arrays - - def scan_vocab(self, sentences, progress_per=10000, trim_rule=None): - """Do an initial scan of all words appearing in sentences.""" - logger.info("collecting all words and their counts") - sentence_no = -1 - total_words = 0 - min_reduce = 1 - vocab = defaultdict(int) - checked_string_types = 0 - for sentence_no, sentence in enumerate(sentences): - if not checked_string_types: - if isinstance(sentence, string_types): - logger.warning( - "Each 'sentences' item should be a list of words (usually unicode strings). " - "First item here is instead plain %s.", - type(sentence) - ) - checked_string_types += 1 - if sentence_no % progress_per == 0: - logger.info( - "PROGRESS: at sentence #%i, processed %i words, keeping %i word types", - sentence_no, total_words, len(vocab) - ) - for word in sentence: - vocab[word] += 1 - total_words += len(sentence) - - 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 from a corpus of %i raw words and %i sentences", - len(vocab), total_words, sentence_no + 1 - ) - self.corpus_count = sentence_no + 1 - self.raw_vocab = vocab - return total_words - - def scale_vocab(self, min_count=None, sample=None, dry_run=False, - keep_raw_vocab=False, trim_rule=None, update=False): - """ - Apply vocabulary settings for `min_count` (discarding less-frequent words) - and `sample` (controlling the downsampling of more-frequent words). - - Calling with `dry_run=True` will only simulate the provided settings and - report the size of the retained vocabulary, effective corpus length, and - estimated memory requirements. Results are both printed via logging and - returned as a dict. - - Delete the raw vocabulary after the scaling is done to free up RAM, - unless `keep_raw_vocab` is set. - - """ - min_count = min_count or self.min_count - sample = sample or self.sample - drop_total = drop_unique = 0 - - if not update: - logger.info("Loading a fresh vocabulary") - retain_total, retain_words = 0, [] - # Discard words less-frequent than min_count - if not dry_run: - self.wv.index2word = [] - # make stored settings match these applied settings - self.min_count = min_count - self.sample = sample - self.wv.vocab = {} - - for word, v in iteritems(self.raw_vocab): - if keep_vocab_item(word, v, min_count, trim_rule=trim_rule): - retain_words.append(word) - retain_total += v - if not dry_run: - self.wv.vocab[word] = Vocab(count=v, index=len(self.wv.index2word)) - self.wv.index2word.append(word) - else: - drop_unique += 1 - drop_total += v - original_unique_total = len(retain_words) + drop_unique - retain_unique_pct = len(retain_words) * 100 / max(original_unique_total, 1) - logger.info( - "min_count=%d retains %i unique words (%i%% of original %i, drops %i)", - min_count, len(retain_words), retain_unique_pct, original_unique_total, drop_unique - ) - original_total = retain_total + drop_total - retain_pct = retain_total * 100 / max(original_total, 1) - logger.info( - "min_count=%d leaves %i word corpus (%i%% of original %i, drops %i)", - min_count, retain_total, retain_pct, original_total, drop_total - ) - else: - logger.info("Updating model with new vocabulary") - new_total = pre_exist_total = 0 - new_words = pre_exist_words = [] - for word, v in iteritems(self.raw_vocab): - if keep_vocab_item(word, v, min_count, trim_rule=trim_rule): - if word in self.wv.vocab: - pre_exist_words.append(word) - pre_exist_total += v - if not dry_run: - self.wv.vocab[word].count += v - else: - new_words.append(word) - new_total += v - if not dry_run: - self.wv.vocab[word] = Vocab(count=v, index=len(self.wv.index2word)) - self.wv.index2word.append(word) - else: - drop_unique += 1 - drop_total += v - original_unique_total = len(pre_exist_words) + len(new_words) + drop_unique - pre_exist_unique_pct = len(pre_exist_words) * 100 / max(original_unique_total, 1) - new_unique_pct = len(new_words) * 100 / max(original_unique_total, 1) - logger.info( - "New added %i unique words (%i%% of original %i) " - "and increased the count of %i pre-existing words (%i%% of original %i)", - len(new_words), new_unique_pct, original_unique_total, len(pre_exist_words), - pre_exist_unique_pct, original_unique_total - ) - retain_words = new_words + pre_exist_words - retain_total = new_total + pre_exist_total - - # Precalculate each vocabulary item's threshold for sampling - if not sample: - # no words downsampled - threshold_count = retain_total - elif sample < 1.0: - # traditional meaning: set parameter as proportion of total - threshold_count = sample * retain_total - else: - # new shorthand: sample >= 1 means downsample all words with higher count than sample - threshold_count = int(sample * (3 + sqrt(5)) / 2) - - downsample_total, downsample_unique = 0, 0 - for w in retain_words: - v = self.raw_vocab[w] - word_probability = (sqrt(v / threshold_count) + 1) * (threshold_count / v) - if word_probability < 1.0: - downsample_unique += 1 - downsample_total += word_probability * v - else: - word_probability = 1.0 - downsample_total += v - if not dry_run: - self.wv.vocab[w].sample_int = int(round(word_probability * 2**32)) - - if not dry_run and not keep_raw_vocab: - logger.info("deleting the raw counts dictionary of %i items", len(self.raw_vocab)) - self.raw_vocab = defaultdict(int) - - logger.info("sample=%g downsamples %i most-common words", sample, downsample_unique) - logger.info( - "downsampling leaves estimated %i word corpus (%.1f%% of prior %i)", - downsample_total, downsample_total * 100.0 / max(retain_total, 1), retain_total - ) - - # return from each step: words-affected, resulting-corpus-size, extra memory estimates - report_values = { - 'drop_unique': drop_unique, 'retain_total': retain_total, 'downsample_unique': downsample_unique, - 'downsample_total': int(downsample_total), 'memory': self.estimate_memory(vocab_size=len(retain_words)) - } - - return report_values - - def finalize_vocab(self, update=False): - """Build tables and model weights based on final vocabulary settings.""" - if not self.wv.index2word: - self.scale_vocab() - if self.sorted_vocab and not update: - self.sort_vocab() - if self.hs: - # add info about each word's Huffman encoding - self.create_binary_tree() - if self.negative: - # build the table for drawing random words (for negative sampling) - self.make_cum_table() - if self.null_word: - # create null pseudo-word for padding when using concatenative L1 (run-of-words) - # this word is only ever input – never predicted – so count, huffman-point, etc doesn't matter - word, v = '\0', Vocab(count=1, sample_int=0) - v.index = len(self.wv.vocab) - self.wv.index2word.append(word) - self.wv.vocab[word] = v - # set initial input/projection and hidden weights - if not update: - self.reset_weights() - else: - self.update_weights() - - def sort_vocab(self): - """Sort the vocabulary so the most frequent words have the lowest indexes.""" - if len(self.wv.syn0): - raise RuntimeError("cannot sort vocabulary after model weights already initialized.") - self.wv.index2word.sort(key=lambda word: self.wv.vocab[word].count, reverse=True) - for i, word in enumerate(self.wv.index2word): - self.wv.vocab[word].index = i - - def reset_from(self, other_model): - """ - Borrow shareable pre-built structures (like vocab) from the other_model. Useful - if testing multiple models in parallel on the same corpus. - """ - self.wv.vocab = other_model.wv.vocab - self.wv.index2word = other_model.wv.index2word - self.cum_table = other_model.cum_table - self.corpus_count = other_model.corpus_count - self.reset_weights() - - def _do_train_job(self, sentences, alpha, inits): - """ - Train a single batch of sentences. Return 2-tuple `(effective word count after - ignoring unknown words and sentence length trimming, total word count)`. - """ - work, neu1 = inits - tally = 0 - if self.sg: - tally += train_batch_sg(self, sentences, alpha, work, self.compute_loss) - else: - tally += train_batch_cbow(self, sentences, alpha, work, neu1, self.compute_loss) - return tally, self._raw_word_count(sentences) - - def _raw_word_count(self, job): - """Return the number of words in a given job.""" - return sum(len(sentence) for sentence in job) - - def train(self, sentences, total_examples=None, total_words=None, - epochs=None, start_alpha=None, end_alpha=None, word_count=0, - queue_factor=2, report_delay=1.0, compute_loss=None): - """ - Update the model's neural weights from a sequence of sentences (can be a once-only generator stream). - For Word2Vec, each sentence must be a list of unicode strings. (Subclasses may accept other examples.) - - To support linear learning-rate decay from (initial) alpha to min_alpha, and accurate - progres-percentage logging, either total_examples (count of sentences) or total_words (count of - raw words in sentences) MUST be provided. (If the corpus is the same as was provided to - `build_vocab()`, the count of examples in that corpus will be available in the model's - `corpus_count` property.) - - To avoid common mistakes around the model's ability to do multiple training passes itself, an - explicit `epochs` argument MUST be provided. In the common and recommended case, where `train()` - is only called once, the model's cached `iter` value should be supplied as `epochs` value. - """ - if self.model_trimmed_post_training: - raise RuntimeError("Parameters for training were discarded using model_trimmed_post_training method") - - if compute_loss: - self.compute_loss = compute_loss - self.running_training_loss = 0 - - logger.info( - "training model with %i workers on %i vocabulary and %i features, " - "using sg=%s hs=%s sample=%s negative=%s window=%s", - self.workers, len(self.wv.vocab), self.layer1_size, self.sg, - self.hs, self.sample, self.negative, self.window - ) - - if not self.wv.vocab: - raise RuntimeError("you must first build vocabulary before training the model") - if not len(self.wv.syn0): - raise RuntimeError("you must first finalize vocabulary before training the model") - - if not hasattr(self, 'corpus_count'): - raise ValueError( - "The number of sentences in the training corpus is missing. " - "Did you load the model via KeyedVectors.load_word2vec_format?" - "Models loaded via load_word2vec_format don't support further training. " - "Instead start with a blank model, scan_vocab on the new corpus, " - "intersect_word2vec_format with the old model, then train." - ) - - if total_words is None and total_examples is None: - raise ValueError( - "You must specify either total_examples or total_words, for proper alpha and progress calculations. " - "The usual value is total_examples=model.corpus_count." - ) - if epochs is None: - raise ValueError("You must specify an explict epochs count. The usual value is epochs=model.iter.") - start_alpha = start_alpha or self.alpha - end_alpha = end_alpha or self.min_alpha - - job_tally = 0 - - if epochs > 1: - sentences = utils.RepeatCorpusNTimes(sentences, epochs) - total_words = total_words and total_words * epochs - total_examples = total_examples and total_examples * epochs - - def worker_loop(): - """Train the model, lifting lists of sentences from the job_queue.""" - work = matutils.zeros_aligned(self.layer1_size, dtype=REAL) # per-thread private work memory - neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL) - jobs_processed = 0 - while True: - job = job_queue.get() - if job is None: - progress_queue.put(None) - break # no more jobs => quit this worker - sentences, alpha = job - tally, raw_tally = self._do_train_job(sentences, alpha, (work, neu1)) - progress_queue.put((len(sentences), tally, raw_tally)) # report back progress - jobs_processed += 1 - logger.debug("worker exiting, processed %i jobs", jobs_processed) - - def job_producer(): - """Fill jobs queue using the input `sentences` iterator.""" - job_batch, batch_size = [], 0 - pushed_words, pushed_examples = 0, 0 - next_alpha = start_alpha - if next_alpha > self.min_alpha_yet_reached: - logger.warning("Effective 'alpha' higher than previous training cycles") - self.min_alpha_yet_reached = next_alpha - job_no = 0 - - for sent_idx, sentence in enumerate(sentences): - sentence_length = self._raw_word_count([sentence]) - - # can we fit this sentence into the existing job batch? - if batch_size + sentence_length <= self.batch_words: - # yes => add it to the current job - job_batch.append(sentence) - batch_size += sentence_length - else: - # no => submit the existing job - logger.debug( - "queueing job #%i (%i words, %i sentences) at alpha %.05f", - job_no, batch_size, len(job_batch), next_alpha - ) - job_no += 1 - job_queue.put((job_batch, next_alpha)) - - # update the learning rate for the next job - if end_alpha < next_alpha: - if total_examples: - # examples-based decay - pushed_examples += len(job_batch) - progress = 1.0 * pushed_examples / total_examples - else: - # words-based decay - pushed_words += self._raw_word_count(job_batch) - progress = 1.0 * pushed_words / total_words - next_alpha = start_alpha - (start_alpha - end_alpha) * progress - next_alpha = max(end_alpha, next_alpha) - - # add the sentence that didn't fit as the first item of a new job - job_batch, batch_size = [sentence], sentence_length - - # add the last job too (may be significantly smaller than batch_words) - if job_batch: - logger.debug( - "queueing job #%i (%i words, %i sentences) at alpha %.05f", - job_no, batch_size, len(job_batch), next_alpha - ) - job_no += 1 - job_queue.put((job_batch, next_alpha)) - - if job_no == 0 and self.train_count == 0: - logger.warning( - "train() called with an empty iterator (if not intended, " - "be sure to provide a corpus that offers restartable iteration = an iterable)." - ) - - # give the workers heads up that they can finish -- no more work! - for _ in range(self.workers): - job_queue.put(None) - logger.debug("job loop exiting, total %i jobs", job_no) - - # buffer ahead only a limited number of jobs.. this is the reason we can't simply use ThreadPool :( - job_queue = Queue(maxsize=queue_factor * self.workers) - progress_queue = Queue(maxsize=(queue_factor + 1) * self.workers) - - workers = [threading.Thread(target=worker_loop) for _ in range(self.workers)] - unfinished_worker_count = len(workers) - workers.append(threading.Thread(target=job_producer)) - - for thread in workers: - thread.daemon = True # make interrupting the process with ctrl+c easier - thread.start() - - example_count, trained_word_count, raw_word_count = 0, 0, word_count - start, next_report = default_timer() - 0.00001, 1.0 - - while unfinished_worker_count > 0: - report = progress_queue.get() # blocks if workers too slow - if report is None: # a thread reporting that it finished - unfinished_worker_count -= 1 - logger.info("worker thread finished; awaiting finish of %i more threads", unfinished_worker_count) - continue - examples, trained_words, raw_words = report - job_tally += 1 - - # update progress stats - example_count += examples - trained_word_count += trained_words # only words in vocab & sampled - raw_word_count += raw_words - - # log progress once every report_delay seconds - elapsed = default_timer() - start - if elapsed >= next_report: - if total_examples: - # examples-based progress % - logger.info( - "PROGRESS: at %.2f%% examples, %.0f words/s, in_qsize %i, out_qsize %i", - 100.0 * example_count / total_examples, trained_word_count / elapsed, - utils.qsize(job_queue), utils.qsize(progress_queue) - ) - else: - # words-based progress % - logger.info( - "PROGRESS: at %.2f%% words, %.0f words/s, in_qsize %i, out_qsize %i", - 100.0 * raw_word_count / total_words, trained_word_count / elapsed, - utils.qsize(job_queue), utils.qsize(progress_queue) - ) - next_report = elapsed + report_delay - - # all done; report the final stats - elapsed = default_timer() - start - logger.info( - "training on %i raw words (%i effective words) took %.1fs, %.0f effective words/s", - raw_word_count, trained_word_count, elapsed, trained_word_count / elapsed - ) - if job_tally < 10 * self.workers: - logger.warning( - "under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay" - ) - - # check that the input corpus hasn't changed during iteration - if total_examples and total_examples != example_count: - logger.warning( - "supplied example count (%i) did not equal expected count (%i)", example_count, total_examples - ) - if total_words and total_words != raw_word_count: - logger.warning( - "supplied raw word count (%i) did not equal expected count (%i)", raw_word_count, total_words - ) - - self.train_count += 1 # number of times train() has been called - self.total_train_time += elapsed - self.clear_sims() - return trained_word_count - - # basics copied from the train() function - def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor=2, report_delay=1): - """ - Score the log probability for a sequence of sentences (can be a once-only generator stream). - Each sentence must be a list of unicode strings. - This does not change the fitted model in any way (see Word2Vec.train() for that). - - We have currently only implemented score for the hierarchical softmax scheme, - so you need to have run word2vec with hs=1 and negative=0 for this to work. - - Note that you should specify total_sentences; we'll run into problems if you ask to - score more than this number of sentences but it is inefficient to set the value too high. - - See the article by [#taddy]_ and the gensim demo at [#deepir]_ for examples of - how to use such scores in document classification. - - .. [#taddy] Taddy, Matt. Document Classification by Inversion of Distributed Language Representations, - in Proceedings of the 2015 Conference of the Association of Computational Linguistics. - .. [#deepir] https://github.com/piskvorky/gensim/blob/develop/docs/notebooks/deepir.ipynb - - """ - logger.info( - "scoring sentences with %i workers on %i vocabulary and %i features, " - "using sg=%s hs=%s sample=%s and negative=%s", - self.workers, len(self.wv.vocab), self.layer1_size, self.sg, self.hs, self.sample, self.negative - ) - - if not self.wv.vocab: - raise RuntimeError("you must first build vocabulary before scoring new data") - - if not self.hs: - raise RuntimeError( - "We have currently only implemented score for the hierarchical softmax scheme, " - "so you need to have run word2vec with hs=1 and negative=0 for this to work." - ) - - def worker_loop(): - """Compute log probability for each sentence, lifting lists of sentences from the jobs queue.""" - work = zeros(1, dtype=REAL) # for sg hs, we actually only need one memory loc (running sum) - neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL) - while True: - job = job_queue.get() - if job is None: # signal to finish - break - ns = 0 - for sentence_id, sentence in job: - if sentence_id >= total_sentences: - break - if self.sg: - score = score_sentence_sg(self, sentence, work) - else: - score = score_sentence_cbow(self, sentence, work, neu1) - sentence_scores[sentence_id] = score - ns += 1 - progress_queue.put(ns) # report progress - - start, next_report = default_timer(), 1.0 - # buffer ahead only a limited number of jobs.. this is the reason we can't simply use ThreadPool :( - job_queue = Queue(maxsize=queue_factor * self.workers) - progress_queue = Queue(maxsize=(queue_factor + 1) * self.workers) - - workers = [threading.Thread(target=worker_loop) for _ in range(self.workers)] - for thread in workers: - thread.daemon = True # make interrupting the process with ctrl+c easier - thread.start() - - sentence_count = 0 - sentence_scores = matutils.zeros_aligned(total_sentences, dtype=REAL) - - push_done = False - done_jobs = 0 - jobs_source = enumerate(utils.grouper(enumerate(sentences), chunksize)) - - # fill jobs queue with (id, sentence) job items - while True: - try: - job_no, items = next(jobs_source) - if (job_no - 1) * chunksize > total_sentences: - logger.warning( - "terminating after %i sentences (set higher total_sentences if you want more).", - total_sentences - ) - job_no -= 1 - raise StopIteration() - logger.debug("putting job #%i in the queue", job_no) - job_queue.put(items) - except StopIteration: - logger.info("reached end of input; waiting to finish %i outstanding jobs", job_no - done_jobs + 1) - for _ in range(self.workers): - job_queue.put(None) # give the workers heads up that they can finish -- no more work! - push_done = True - try: - while done_jobs < (job_no + 1) or not push_done: - ns = progress_queue.get(push_done) # only block after all jobs pushed - sentence_count += ns - done_jobs += 1 - elapsed = default_timer() - start - if elapsed >= next_report: - logger.info( - "PROGRESS: at %.2f%% sentences, %.0f sentences/s", - 100.0 * sentence_count, sentence_count / elapsed - ) - next_report = elapsed + report_delay # don't flood log, wait report_delay seconds - else: - # loop ended by job count; really done - break - except Empty: - pass # already out of loop; continue to next push - - elapsed = default_timer() - start - self.clear_sims() - logger.info( - "scoring %i sentences took %.1fs, %.0f sentences/s", - sentence_count, elapsed, sentence_count / elapsed - ) - return sentence_scores[:sentence_count] - - def clear_sims(self): - """ - Removes all L2-normalized vectors for words from the model. - You will have to recompute them using init_sims method. - """ - - self.wv.syn0norm = None - - def update_weights(self): - """ - Copy all the existing weights, and reset the weights for the newly - added vocabulary. - """ - logger.info("updating layer weights") - gained_vocab = len(self.wv.vocab) - len(self.wv.syn0) - newsyn0 = empty((gained_vocab, self.vector_size), dtype=REAL) - - # randomize the remaining words - for i in range(len(self.wv.syn0), len(self.wv.vocab)): - # construct deterministic seed from word AND seed argument - newsyn0[i - len(self.wv.syn0)] = self.seeded_vector(self.wv.index2word[i] + str(self.seed)) - - # Raise an error if an online update is run before initial training on a corpus - if not len(self.wv.syn0): - raise RuntimeError( - "You cannot do an online vocabulary-update of a model which has no prior vocabulary. " - "First build the vocabulary of your model with a corpus before doing an online update." - ) - - self.wv.syn0 = vstack([self.wv.syn0, newsyn0]) - - if self.hs: - self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=REAL)]) - if self.negative: - self.syn1neg = vstack([self.syn1neg, zeros((gained_vocab, self.layer1_size), dtype=REAL)]) - self.wv.syn0norm = None - - # do not suppress learning for already learned words - self.syn0_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning - - def reset_weights(self): - """Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary.""" - logger.info("resetting layer weights") - self.wv.syn0 = empty((len(self.wv.vocab), self.vector_size), dtype=REAL) - # randomize weights vector by vector, rather than materializing a huge random matrix in RAM at once - for i in range(len(self.wv.vocab)): - # construct deterministic seed from word AND seed argument - self.wv.syn0[i] = self.seeded_vector(self.wv.index2word[i] + str(self.seed)) - if self.hs: - self.syn1 = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL) - if self.negative: - self.syn1neg = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL) - self.wv.syn0norm = None - - self.syn0_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning - - def seeded_vector(self, seed_string): - """Create one 'random' vector (but deterministic by seed_string)""" - # Note: built-in hash() may vary by Python version or even (in Py3.x) per launch - once = random.RandomState(self.hashfxn(seed_string) & 0xffffffff) - return (once.rand(self.vector_size) - 0.5) / self.vector_size - - def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='utf8', unicode_errors='strict'): - """ - Merge the input-hidden weight matrix from the original C word2vec-tool format - given, where it intersects with the current vocabulary. (No words are added to the - existing vocabulary, but intersecting words adopt the file's weights, and - non-intersecting words are left alone.) - - `binary` is a boolean indicating whether the data is in binary word2vec format. - - `lockf` is a lock-factor value to be set for any imported word-vectors; the - default value of 0.0 prevents further updating of the vector during subsequent - training. Use 1.0 to allow further training updates of merged vectors. - """ - overlap_count = 0 - logger.info("loading projection weights from %s", fname) - with utils.open(fname, 'rb') as fin: - header = utils.to_unicode(fin.readline(), encoding=encoding) - vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format - if not vector_size == self.vector_size: - raise ValueError("incompatible vector size %d in file %s" % (vector_size, fname)) - # TOCONSIDER: maybe mismatched vectors still useful enough to merge (truncating/padding)? - if binary: - binary_len = dtype(REAL).itemsize * vector_size - for _ in range(vocab_size): - # mixed text and binary: read text first, then binary - word = [] - while True: - ch = fin.read(1) - if ch == b' ': - break - if ch != b'\n': # ignore newlines in front of words (some binary files have) - word.append(ch) - word = utils.to_unicode(b''.join(word), encoding=encoding, errors=unicode_errors) - weights = fromstring(fin.read(binary_len), dtype=REAL) - if word in self.wv.vocab: - overlap_count += 1 - self.wv.syn0[self.wv.vocab[word].index] = weights - self.syn0_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0 stops further changes - else: - for line_no, line in enumerate(fin): - parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") - if len(parts) != vector_size + 1: - raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) - word, weights = parts[0], [REAL(x) for x in parts[1:]] - if word in self.wv.vocab: - overlap_count += 1 - self.wv.syn0[self.wv.vocab[word].index] = weights - self.syn0_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0 stops further changes - logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.syn0.shape, fname) - - def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=None, indexer=None): - """ - Deprecated. Use self.wv.most_similar() instead. - Refer to the documentation for `gensim.models.KeyedVectors.most_similar` - """ - return self.wv.most_similar(positive, negative, topn, restrict_vocab, indexer) - - def wmdistance(self, document1, document2): - """ - Deprecated. Use self.wv.wmdistance() instead. - Refer to the documentation for `gensim.models.KeyedVectors.wmdistance` - """ - return self.wv.wmdistance(document1, document2) - - def most_similar_cosmul(self, positive=None, negative=None, topn=10): - """ - Deprecated. Use self.wv.most_similar_cosmul() instead. - Refer to the documentation for `gensim.models.KeyedVectors.most_similar_cosmul` - """ - return self.wv.most_similar_cosmul(positive, negative, topn) - - def similar_by_word(self, word, topn=10, restrict_vocab=None): - """ - Deprecated. Use self.wv.similar_by_word() instead. - Refer to the documentation for `gensim.models.KeyedVectors.similar_by_word` - """ - return self.wv.similar_by_word(word, topn, restrict_vocab) - - def similar_by_vector(self, vector, topn=10, restrict_vocab=None): - """ - Deprecated. Use self.wv.similar_by_vector() instead. - Refer to the documentation for `gensim.models.KeyedVectors.similar_by_vector` - """ - return self.wv.similar_by_vector(vector, topn, restrict_vocab) - - def doesnt_match(self, words): - """ - Deprecated. Use self.wv.doesnt_match() instead. - Refer to the documentation for `gensim.models.KeyedVectors.doesnt_match` - """ - return self.wv.doesnt_match(words) - - def __getitem__(self, words): - """ - Deprecated. Use self.wv.__getitem__() instead. - Refer to the documentation for `gensim.models.KeyedVectors.__getitem__` - """ - return self.wv.__getitem__(words) - - def __contains__(self, word): - """ - Deprecated. Use self.wv.__contains__() instead. - Refer to the documentation for `gensim.models.KeyedVectors.__contains__` - """ - return self.wv.__contains__(word) - - def similarity(self, w1, w2): - """ - Deprecated. Use self.wv.similarity() instead. - Refer to the documentation for `gensim.models.KeyedVectors.similarity` - """ - return self.wv.similarity(w1, w2) - - def n_similarity(self, ws1, ws2): - """ - Deprecated. Use self.wv.n_similarity() instead. - Refer to the documentation for `gensim.models.KeyedVectors.n_similarity` - """ - return self.wv.n_similarity(ws1, ws2) - - def predict_output_word(self, context_words_list, topn=10): - """Report the probability distribution of the center word given the context words - as input to the trained model.""" - if not self.negative: - raise RuntimeError( - "We have currently only implemented predict_output_word for the negative sampling scheme, " - "so you need to have run word2vec with negative > 0 for this to work." - ) - - if not hasattr(self.wv, 'syn0') or not hasattr(self, 'syn1neg'): - raise RuntimeError("Parameters required for predicting the output words not found.") - - word_vocabs = [self.wv.vocab[w] for w in context_words_list if w in self.wv.vocab] - if not word_vocabs: - warnings.warn("All the input context words are out-of-vocabulary for the current model.") - return None - - word2_indices = [word.index for word in word_vocabs] - - l1 = np_sum(self.wv.syn0[word2_indices], axis=0) - if word2_indices and self.cbow_mean: - l1 /= len(word2_indices) - - prob_values = exp(dot(l1, self.syn1neg.T)) # propagate hidden -> output and take softmax to get probabilities - prob_values /= sum(prob_values) - top_indices = matutils.argsort(prob_values, topn=topn, reverse=True) - # returning the most probable output words with their probabilities - return [(self.wv.index2word[index1], prob_values[index1]) for index1 in top_indices] - - def init_sims(self, replace=False): - """ - init_sims() resides in KeyedVectors because it deals with syn0 mainly, but because syn1 is not an attribute - of KeyedVectors, it has to be deleted in this class, and the normalizing of syn0 happens inside of KeyedVectors - """ - if replace and hasattr(self, 'syn1'): - del self.syn1 - return self.wv.init_sims(replace) - - def estimate_memory(self, vocab_size=None, report=None): - """Estimate required memory for a model using current settings and provided vocabulary size.""" - vocab_size = vocab_size or len(self.wv.vocab) - report = report or {} - report['vocab'] = vocab_size * (700 if self.hs else 500) - report['syn0'] = vocab_size * self.vector_size * dtype(REAL).itemsize - if self.hs: - report['syn1'] = vocab_size * self.layer1_size * dtype(REAL).itemsize - if self.negative: - report['syn1neg'] = vocab_size * self.layer1_size * dtype(REAL).itemsize - report['total'] = sum(report.values()) - logger.info( - "estimated required memory for %i words and %i dimensions: %i bytes", - vocab_size, self.vector_size, report['total'] - ) - return report - - @staticmethod - def log_accuracy(section): - return KeyedVectors.log_accuracy(section) - - def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_insensitive=True): - most_similar = most_similar or KeyedVectors.most_similar - return self.wv.accuracy(questions, restrict_vocab, most_similar, case_insensitive) - - @staticmethod - def log_evaluate_word_pairs(pearson, spearman, oov, pairs): - """ - Deprecated. Use self.wv.log_evaluate_word_pairs() instead. - Refer to the documentation for `gensim.models.KeyedVectors.log_evaluate_word_pairs` - """ - return KeyedVectors.log_evaluate_word_pairs(pearson, spearman, oov, pairs) - - def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, - case_insensitive=True, dummy4unknown=False): - """ - Deprecated. Use self.wv.evaluate_word_pairs() instead. - Refer to the documentation for `gensim.models.KeyedVectors.evaluate_word_pairs` - """ - return self.wv.evaluate_word_pairs(pairs, delimiter, restrict_vocab, case_insensitive, dummy4unknown) - - def __str__(self): - return "%s(vocab=%s, size=%s, alpha=%s)" % ( - self.__class__.__name__, len(self.wv.index2word), self.vector_size, self.alpha - ) - - def _minimize_model(self, save_syn1=False, save_syn1neg=False, save_syn0_lockf=False): - warnings.warn( - "This method would be deprecated in the future. " - "Keep just_word_vectors = model.wv to retain just the KeyedVectors instance " - "for read-only querying of word vectors." - ) - if save_syn1 and save_syn1neg and save_syn0_lockf: - return - if hasattr(self, 'syn1') and not save_syn1: - del self.syn1 - if hasattr(self, 'syn1neg') and not save_syn1neg: - del self.syn1neg - if hasattr(self, 'syn0_lockf') and not save_syn0_lockf: - del self.syn0_lockf - self.model_trimmed_post_training = True - - def delete_temporary_training_data(self, replace_word_vectors_with_normalized=False): - """ - Discard parameters that are used in training and score. Use if you're sure you're done training a model. - If `replace_word_vectors_with_normalized` is set, forget the original vectors and only keep the normalized - ones = saves lots of memory! - """ - if replace_word_vectors_with_normalized: - self.init_sims(replace=True) - self._minimize_model() - - def save(self, *args, **kwargs): - # don't bother storing the cached normalized vectors, recalculable table - kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'table', 'cum_table']) - - super(Word2Vec, self).save(*args, **kwargs) - - save.__doc__ = SaveLoad.save.__doc__ - - @classmethod - def load(cls, *args, **kwargs): - model = super(Word2Vec, cls).load(*args, **kwargs) - # update older models - if hasattr(model, 'table'): - delattr(model, 'table') # discard in favor of cum_table - if model.negative and hasattr(model.wv, 'index2word'): - model.make_cum_table() # rebuild cum_table from vocabulary - if not hasattr(model, 'corpus_count'): - model.corpus_count = None - if not hasattr(model, 'corpus_total_words'): - model.corpus_total_words = None - for v in model.wv.vocab.values(): - if hasattr(v, 'sample_int'): - break # already 0.12.0+ style int probabilities - elif hasattr(v, 'sample_probability'): - v.sample_int = int(round(v.sample_probability * 2**32)) - del v.sample_probability - if not hasattr(model, 'syn0_lockf') and hasattr(model, 'syn0'): - model.syn0_lockf = ones(len(model.wv.syn0), dtype=REAL) - if not hasattr(model, 'random'): - model.random = random.RandomState(model.seed) - if not hasattr(model, 'train_count'): - model.train_count = 0 - model.total_train_time = 0 - return model - - def _load_specials(self, *args, **kwargs): - super(Word2Vec, self)._load_specials(*args, **kwargs) - # loading from a pre-KeyedVectors word2vec model - if not hasattr(self, 'wv'): - wv = KeyedVectors() - wv.syn0 = self.__dict__.get('syn0', []) - wv.syn0norm = self.__dict__.get('syn0norm', None) - wv.vocab = self.__dict__.get('vocab', {}) - wv.index2word = self.__dict__.get('index2word', []) - self.wv = wv - - @classmethod - def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', - limit=None, datatype=REAL): - """Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.""" - raise DeprecationWarning("Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.") - - def save_word2vec_format(self, fname, fvocab=None, binary=False): - """Deprecated. Use model.wv.save_word2vec_format instead.""" - raise DeprecationWarning("Deprecated. Use model.wv.save_word2vec_format instead.") - - def get_latest_training_loss(self): - return self.running_training_loss - - -class BrownCorpus(object): - """Iterate over sentences from the Brown corpus (part of NLTK data).""" - - 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 - with utils.open(fname, 'rb') as fin: - for line in fin: - line = utils.to_unicode(line) - # each file line is a single sentence 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 sentences - continue - yield words - - -class Text8Corpus(object): - """Iterate over sentences from the "text8" corpus, unzipped from http://mattmahoney.net/dc/text8.zip .""" - - def __init__(self, fname, max_sentence_length=MAX_WORDS_IN_BATCH): - self.fname = fname - self.max_sentence_length = max_sentence_length - - def __iter__(self): - # the entire corpus is one gigantic line -- there are no sentence marks at all - # so just split the sequence of tokens arbitrarily: 1 sentence = 1000 tokens - sentence, rest = [], b'' - with utils.open(self.fname, 'rb') as fin: - while True: - text = rest + fin.read(8192) # avoid loading the entire file (=1 line) into RAM - if text == rest: # EOF - words = utils.to_unicode(text).split() - sentence.extend(words) # return the last chunk of words, too (may be shorter/longer) - if sentence: - yield sentence - break - last_token = text.rfind(b' ') # last token may have been split in two... keep for next iteration - words, rest = (utils.to_unicode(text[:last_token]).split(), - text[last_token:].strip()) if last_token >= 0 else ([], text) - sentence.extend(words) - while len(sentence) >= self.max_sentence_length: - yield sentence[:self.max_sentence_length] - sentence = sentence[self.max_sentence_length:] - - -class LineSentence(object): - """ - Simple format: one sentence = one line; words already preprocessed and separated by whitespace. - """ - - def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None): - """ - `source` can be either a string or a file object. Clip the file to the first - `limit` lines (or not clipped if limit is None, the default). - - Example:: - - sentences = LineSentence('myfile.txt') - - Or for compressed files:: - - sentences = LineSentence('compressed_text.txt.bz2') - sentences = LineSentence('compressed_text.txt.gz') - - """ - self.source = source - self.max_sentence_length = max_sentence_length - self.limit = limit - - 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 line in itertools.islice(self.source, self.limit): - line = utils.to_unicode(line).split() - i = 0 - while i < len(line): - yield line[i: i + self.max_sentence_length] - i += self.max_sentence_length - except AttributeError: - # If it didn't work like a file, use it as a string filename - with utils.open(self.source, 'rb') as fin: - for line in itertools.islice(fin, self.limit): - line = utils.to_unicode(line).split() - i = 0 - while i < len(line): - yield line[i: i + self.max_sentence_length] - i += self.max_sentence_length - - -class PathLineSentences(object): - """ - - Works like word2vec.LineSentence, but will process all files in a directory in alphabetical order by filename. - The directory can only contain files that can be read by LineSentence: .bz2, .gz, and text files. - Any file not ending with .bz2 or .gz is assumed to be a text file. Does not work with subdirectories. - - The format of files (either text, or compressed text files) in the path is one sentence = one line, - with words already preprocessed and separated by whitespace. - - """ - - def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None): - """ - `source` should be a path to a directory (as a string) where all files can be opened by the - LineSentence class. Each file will be read up to `limit` lines (or not clipped if limit is None, the default). - - Example:: - - sentences = PathLineSentences(os.getcwd() + '\\corpus\\') - - The files in the directory should be either text files, .bz2 files, or .gz files. - - """ - self.source = source - self.max_sentence_length = max_sentence_length - self.limit = limit - - if os.path.isfile(self.source): - logger.debug('single file given as source, rather than a directory of files') - logger.debug('consider using models.word2vec.LineSentence for a single file') - self.input_files = [self.source] # force code compatibility with list of files - elif os.path.isdir(self.source): - self.source = os.path.join(self.source, '') # ensures os-specific slash at end of path - logger.info('reading directory %s', self.source) - self.input_files = os.listdir(self.source) - self.input_files = [self.source + filename for filename in self.input_files] # make full paths - self.input_files.sort() # makes sure it happens in filename order - else: # not a file or a directory, then we can't do anything with it - raise ValueError('input is neither a file nor a path') - logger.info('files read into PathLineSentences:%s', '\n'.join(self.input_files)) - - def __iter__(self): - """iterate through the files""" - for file_name in self.input_files: - logger.info('reading file %s', file_name) - with utils.open(file_name, 'rb') as fin: - for line in itertools.islice(fin, self.limit): - line = utils.to_unicode(line).split() - i = 0 - while i < len(line): - yield line[i:i + self.max_sentence_length] - i += self.max_sentence_length - - -# Example: ./word2vec.py -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 \ -# -negative 5 -hs 0 -binary 0 -cbow 1 -iter 3 -if __name__ == "__main__": - import argparse - logging.basicConfig( - format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s', - level=logging.INFO - ) - logger.info("running %s", " ".join(sys.argv)) - - # check and process cmdline input - program = os.path.basename(sys.argv[0]) - if len(sys.argv) < 2: - print(globals()['__doc__'] % locals()) - sys.exit(1) - - from gensim.models.word2vec import Word2Vec # noqa:F811 avoid referencing __main__ in pickle - - seterr(all='raise') # don't ignore numpy errors - - parser = argparse.ArgumentParser() - parser.add_argument("-train", help="Use text data from file TRAIN to train the model", required=True) - parser.add_argument("-output", help="Use file OUTPUT to save the resulting word vectors") - parser.add_argument("-window", help="Set max skip length WINDOW between words; default is 5", type=int, default=5) - parser.add_argument("-size", help="Set size of word vectors; default is 100", type=int, default=100) - parser.add_argument( - "-sample", - help="Set threshold for occurrence of words. " - "Those that appear with higher frequency in the training data will be randomly down-sampled;" - " default is 1e-3, useful range is (0, 1e-5)", - type=float, default=1e-3 - ) - parser.add_argument( - "-hs", help="Use Hierarchical Softmax; default is 0 (not used)", - type=int, default=0, choices=[0, 1] - ) - parser.add_argument( - "-negative", help="Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)", - type=int, default=5 - ) - parser.add_argument("-threads", help="Use THREADS threads (default 12)", type=int, default=12) - parser.add_argument("-iter", help="Run more training iterations (default 5)", type=int, default=5) - parser.add_argument( - "-min_count", help="This will discard words that appear less than MIN_COUNT times; default is 5", - type=int, default=5 - ) - parser.add_argument( - "-cbow", help="Use the continuous bag of words model; default is 1 (use 0 for skip-gram model)", - type=int, default=1, choices=[0, 1] - ) - parser.add_argument( - "-binary", help="Save the resulting vectors in binary mode; default is 0 (off)", - type=int, default=0, choices=[0, 1] - ) - parser.add_argument("-accuracy", help="Use questions from file ACCURACY to evaluate the model") - - args = parser.parse_args() - - if args.cbow == 0: - skipgram = 1 - else: - skipgram = 0 - - corpus = LineSentence(args.train) - - model = Word2Vec( - corpus, size=args.size, min_count=args.min_count, workers=args.threads, - window=args.window, sample=args.sample, sg=skipgram, hs=args.hs, - negative=args.negative, cbow_mean=1, iter=args.iter - ) - - if args.output: - outfile = args.output - model.wv.save_word2vec_format(outfile, binary=args.binary) - else: - outfile = args.train - model.save(outfile + '.model') - if args.binary == 1: - model.wv.save_word2vec_format(outfile + '.model.bin', binary=True) - else: - model.wv.save_word2vec_format(outfile + '.model.txt', binary=False) - - if args.accuracy: - model.accuracy(args.accuracy) - - logger.info("finished running %s", program) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index ee619f0540..4a2a1761ac 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # -# Author: Shiva Manne +# Author: Gensim Contributors # Copyright (C) 2018 RaRe Technologies s.r.o. # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html @@ -78,14 +78,11 @@ memmap as np_memmap, vstack, integer, dtype import numpy as np -from gensim.utils import call_on_class_only from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc -from gensim.models.word2vec import Word2VecVocab, Word2VecTrainables +from gensim.models import Word2Vec from six.moves import range from six import string_types, integer_types, itervalues -from gensim.models.base_any2vec import BaseWordEmbeddingsModel from gensim.models.keyedvectors import KeyedVectors, ConcatList, pseudorandom_weak_vector -from types import GeneratorType logger = logging.getLogger(__name__) @@ -171,10 +168,10 @@ def count(self, new_val): Doctag = DoctagVocab -class Doc2Vec(BaseWordEmbeddingsModel): - def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, +class Doc2Vec(Word2Vec): + def __init__(self, documents=None, corpus_file=None, vector_size=100, 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, callbacks=(), - **kwargs): + window=5, epochs=10, **kwargs): """Class for training, using and evaluating neural networks described in `Distributed Representations of Sentences and Documents `_. @@ -220,7 +217,7 @@ def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_wo workers : int, optional Use these many worker threads to train the model (=faster training with multicore machines). epochs : int, optional - Number of iterations (epochs) over the corpus. + Number of iterations (epochs) over the corpus. Defaults to 10 for Doc2Vec. hs : {1,0}, optional If 1, hierarchical softmax will be used for model training. If set to 0, and `negative` is non-zero, negative sampling will be used. @@ -281,28 +278,8 @@ def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_wo .. sourcecode:: pycon >>> model.docvecs['doc003'] - - vocabulary : :class:`~gensim.models.doc2vec.Doc2VecVocab` - This object represents the vocabulary (sometimes called Dictionary in gensim) of the model. - Besides keeping track of all unique words, this object provides extra functionality, such as - sorting words by frequency, or discarding extremely rare words. - - trainables : :class:`~gensim.models.doc2vec.Doc2VecTrainables` - This object represents the inner shallow neural network used to train the embeddings. The semantics - of the network differ slightly in the two available training modes (CBOW or SG) but you can think - of it as a NN with a single projection and hidden layer which we train on the corpus. The weights are - then used as our embeddings. The only addition to the underlying NN used in - :class:`~gensim.models.word2vec.Word2Vec` is that the input includes not only the word vectors of - each word in the context, but also the paragraph vector. - """ - super(Doc2Vec, self).__init__( - sg=(1 + dm) % 2, - null_word=dm_concat, - callbacks=callbacks, - **kwargs) - - self.load = call_on_class_only + corpus_iterable = documents if dm_mean is not None: self.cbow_mean = dm_mean @@ -310,34 +287,23 @@ def __init__(self, documents=None, corpus_file=None, dm_mean=None, dm=1, dbow_wo self.dbow_words = int(dbow_words) self.dm_concat = int(dm_concat) self.dm_tag_count = int(dm_tag_count) + if dm and dm_concat: + self.layer1_size = (dm_tag_count + (2 * window)) * vector_size + logger.info("using concatenative %d-dimensional layer1", self.layer1_size) - kwargs['null_word'] = dm_concat - vocabulary_keys = ['max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'ns_exponent'] - vocabulary_kwargs = dict((k, kwargs[k]) for k in vocabulary_keys if k in kwargs) - self.vocabulary = Doc2VecVocab(**vocabulary_kwargs) - - trainables_keys = ['seed', 'hashfxn', 'window'] - trainables_kwargs = dict((k, kwargs[k]) for k in trainables_keys if k in kwargs) - self.trainables = Doc2VecTrainables( - dm=dm, dm_concat=dm_concat, dm_tag_count=dm_tag_count, - vector_size=self.vector_size, **trainables_kwargs) - - self.wv = KeyedVectors(self.vector_size) + self.vector_size = vector_size self.docvecs = docvecs or KeyedVectors(self.vector_size, mapfile_path=docvecs_mapfile) - self.comment = comment - - if documents is not None or corpus_file is not None: - self._check_input_data_sanity(data_iterable=documents, corpus_file=corpus_file) - if corpus_file is not None and not isinstance(corpus_file, string_types): - raise TypeError("You must pass string as the corpus_file argument.") - elif isinstance(documents, GeneratorType): - raise TypeError("You can't pass a generator as the documents argument. Try a sequence.") - self.build_vocab(documents=documents, corpus_file=corpus_file, trim_rule=trim_rule) - self.train( - documents=documents, corpus_file=corpus_file, total_examples=self.corpus_count, - total_words=self.corpus_total_words, epochs=self.epochs, start_alpha=self.alpha, - end_alpha=self.min_alpha, callbacks=callbacks) + super(Doc2Vec, self).__init__( + sentences=corpus_iterable, + corpus_file=corpus_file, + vector_size=self.vector_size, + sg=(1 + dm) % 2, + null_word=self.dm_concat, + callbacks=callbacks, + window=window, + epochs=epochs, + **kwargs) @property def dm(self): @@ -355,9 +321,6 @@ def dbow(self): """ return self.sg # same as SG - def _set_train_params(self, **kwargs): - pass - def _clear_post_train(self): """Alias for :meth:`~gensim.models.doc2vec.Doc2Vec.clear_sims`.""" self.clear_sims() @@ -367,6 +330,18 @@ def clear_sims(self): self.wv.vectors_norm = None self.docvecs.vectors_norm = None + def reset_weights(self): + super(Doc2Vec, self).reset_weights() + self.docvecs.resize_vectors() + self.docvecs.randomly_initialize_vectors() + if self.docvecs.mapfile_path: + self.docvecs.vectors_lockf = np_memmap( + self.docvecs.mapfile_path + '.vectors_lockf', dtype=REAL, mode='w+', shape=(len(self.docvecs.vectors),) + ) + self.docvecs.vectors_lockf.fill(1.0) + else: + self.docvecs.vectors_lockf = ones((len(self.docvecs.vectors),), dtype=REAL) # zeros suppress learning + def reset_from(self, other_model): """Copy shareable data structures from another (possibly pre-trained) model. @@ -378,17 +353,17 @@ def reset_from(self, other_model): """ self.wv.vocab = other_model.wv.vocab self.wv.index2key = other_model.wv.index2key - self.vocabulary.cum_table = other_model.vocabulary.cum_table + self.cum_table = other_model.cum_table self.corpus_count = other_model.corpus_count self.docvecs.vocab = other_model.docvecs.vocab self.docvecs.index2key = other_model.docvecs.index2key - self.trainables.reset_weights(self.hs, self.negative, self.wv, self.docvecs) + self.reset_weights() def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, total_examples=None, total_words=None, offsets=None, start_doctags=None, **kwargs): work, neu1 = thread_private_mem doctag_vectors = self.docvecs.vectors - doctag_locks = self.trainables.vectors_docs_lockf + doctag_locks = self.docvecs.vectors_lockf offset = offsets[thread_id] start_doctag = start_doctags[thread_id] @@ -434,7 +409,7 @@ def _do_train_job(self, job, alpha, inits): for doc in job: doctag_indexes = [self.docvecs.get_index(tag) for tag in doc.tags if tag in self.docvecs] doctag_vectors = self.docvecs.vectors - doctag_locks = self.trainables.vectors_docs_lockf + doctag_locks = self.docvecs.vectors_lockf if self.sg: tally += train_document_dbow( self, doc.words, doctag_indexes, alpha, work, train_words=self.dbow_words, @@ -452,9 +427,10 @@ def _do_train_job(self, job, alpha, inits): ) return tally, self._raw_word_count(job) - def train(self, documents=None, corpus_file=None, total_examples=None, total_words=None, + def train(self, corpus_iterable=None, corpus_file=None, total_examples=None, total_words=None, epochs=None, start_alpha=None, end_alpha=None, - word_count=0, queue_factor=2, report_delay=1.0, callbacks=()): + word_count=0, queue_factor=2, report_delay=1.0, callbacks=(), + **kwargs): """Update the model's neural weights. To support linear learning-rate decay from (initial) `alpha` to `min_alpha`, and accurate @@ -470,7 +446,7 @@ def train(self, documents=None, corpus_file=None, total_examples=None, total_wor Parameters ---------- - documents : iterable of list of :class:`~gensim.models.doc2vec.TaggedDocument`, optional + corpus_iterable : iterable of list of :class:`~gensim.models.doc2vec.TaggedDocument`, optional Can be simply a list of elements, but for larger corpora,consider an iterable that streams the documents directly from disk/network. If you don't supply `documents` (or `corpus_file`), the model is left uninitialized -- use if you plan to initialize it in some other way. @@ -507,19 +483,17 @@ def train(self, documents=None, corpus_file=None, total_examples=None, total_wor List of callbacks that need to be executed/run at specific stages during training. """ - kwargs = {} - - if corpus_file is None and documents is None: + if corpus_file is None and corpus_iterable is None: raise TypeError("Either one of corpus_file or documents value must be provided") - if corpus_file is not None and documents is not None: - raise TypeError("Both corpus_file and documents must not be provided at the same time") + if corpus_file is not None and corpus_iterable is not None: + raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time") - if documents is None and not os.path.isfile(corpus_file): + if corpus_iterable is None and not os.path.isfile(corpus_file): raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file) - if documents is not None and not isinstance(documents, Iterable): - raise TypeError("documents must be an iterable of list, got %r instead" % documents) + if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable): + raise TypeError("corpus_iterable must be an iterable of TaggedDocument, got %r instead" % corpus_iterable) if corpus_file is not None: # Calculate offsets for each worker along with initial doctags (doctag ~ document/line number in a file) @@ -528,7 +502,8 @@ def train(self, documents=None, corpus_file=None, total_examples=None, total_wor kwargs['start_doctags'] = start_doctags super(Doc2Vec, self).train( - sentences=documents, corpus_file=corpus_file, total_examples=total_examples, total_words=total_words, + corpus_iterable=corpus_iterable, corpus_file=corpus_file, + total_examples=total_examples, total_words=total_words, epochs=epochs, start_alpha=start_alpha, end_alpha=end_alpha, word_count=word_count, queue_factor=queue_factor, report_delay=report_delay, callbacks=callbacks, **kwargs) @@ -643,9 +618,9 @@ def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps doctag_locks = np.ones(1, dtype=REAL) doctag_indexes = [0] - work = zeros(self.trainables.layer1_size, dtype=REAL) + work = zeros(self.layer1_size, dtype=REAL) if not self.sg: - neu1 = matutils.zeros_aligned(self.trainables.layer1_size, dtype=REAL) + neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL) alpha_delta = (alpha - min_alpha) / max(epochs - 1, 1) @@ -722,10 +697,10 @@ def __str__(self): 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.vocabulary.min_count > 1: - segments.append('mc%d' % self.vocabulary.min_count) - if self.vocabulary.sample > 0: - segments.append('s%g' % self.vocabulary.sample) + 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)) @@ -789,9 +764,9 @@ def load(cls, *args, **kwargs): fname : str Path to the saved file. *args : object - Additional arguments, see `~gensim.models.base_any2vec.BaseWordEmbeddingsModel.load`. + Additional arguments, see `~gensim.models.word2vec.Word2Vec.load`. **kwargs : object - Additional arguments, see `~gensim.models.base_any2vec.BaseWordEmbeddingsModel.load`. + Additional arguments, see `~gensim.models.word2vec.Word2Vec.load`. See Also -------- @@ -805,11 +780,13 @@ def load(cls, *args, **kwargs): """ try: - return super(Doc2Vec, cls).load(*args, **kwargs) - except AttributeError: - logger.info('Model saved using code from earlier Gensim Version. Re-loading old model in a compatible way.') - from gensim.models.deprecated.doc2vec import load_old_doc2vec - return load_old_doc2vec(*args, **kwargs) + return super(Doc2Vec, cls).load(*args, rethrow=True, **kwargs) + except AttributeError as ae: + logger.error( + "Model load error. Was model saved using code from an older Gensim Version? " + "Try loading older model using gensim-3.8.1, then re-saving, to restore " + "compatibility with current code.") + raise ae def estimate_memory(self, vocab_size=None, report=None): """Estimate required memory for a model using current settings. @@ -835,8 +812,8 @@ def estimate_memory(self, vocab_size=None, report=None): report['doctag_syn0'] = len(self.docvecs) * self.vector_size * dtype(REAL).itemsize return super(Doc2Vec, self).estimate_memory(vocab_size, report=report) - def build_vocab(self, documents=None, corpus_file=None, update=False, progress_per=10000, keep_raw_vocab=False, - trim_rule=None, **kwargs): + def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, progress_per=10000, + keep_raw_vocab=False, trim_rule=None, **kwargs): """Build vocabulary from a sequence of documents (can be a once-only generator stream). Parameters @@ -874,19 +851,16 @@ def build_vocab(self, documents=None, corpus_file=None, update=False, progress_p Additional key word arguments passed to the internal vocabulary construction. """ - total_words, corpus_count = self.vocabulary.scan_vocab( - documents=documents, corpus_file=corpus_file, docvecs=self.docvecs, + total_words, corpus_count = self.scan_vocab( + corpus_iterable=corpus_iterable, corpus_file=corpus_file, docvecs=self.docvecs, progress_per=progress_per, trim_rule=trim_rule ) self.corpus_count = corpus_count self.corpus_total_words = total_words - report_values = self.vocabulary.prepare_vocab( - self.hs, self.negative, self.wv, update=update, keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, - **kwargs) + report_values = self.prepare_vocab(update=update, keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, **kwargs) report_values['memory'] = self.estimate_memory(vocab_size=report_values['num_retained_words']) - self.trainables.prepare_weights( - self.hs, self.negative, self.wv, self.docvecs, update=update) + self.prepare_weights(update=update) def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=None, trim_rule=None, update=False): """Build vocabulary from a dictionary of word frequencies. @@ -931,80 +905,14 @@ def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=No # Since no documents are provided, this is to control the corpus_count self.corpus_count = corpus_count or 0 - self.vocabulary.raw_vocab = raw_vocab + self.raw_vocab = raw_vocab # trim by min_count & precalculate downsampling - report_values = self.vocabulary.prepare_vocab( - self.hs, self.negative, self.wv, keep_raw_vocab=keep_raw_vocab, - trim_rule=trim_rule, update=update) + report_values = self.prepare_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update) report_values['memory'] = self.estimate_memory(vocab_size=report_values['num_retained_words']) - self.trainables.prepare_weights( - self.hs, self.negative, self.wv, self.docvecs, update=update) - - def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=None, steps=None): - """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 = self.infer_vector(doc_words=doc_words1, alpha=alpha, min_alpha=min_alpha, steps=steps) - d2 = self.infer_vector(doc_words=doc_words2, alpha=alpha, min_alpha=min_alpha, steps=steps) - return np.dot(matutils.unitvec(d1), matutils.unitvec(d2)) - + self.prepare_weights(update=update) -class Doc2VecVocab(Word2VecVocab): - def __init__(self, max_vocab_size=None, min_count=5, sample=1e-3, sorted_vocab=True, null_word=0, ns_exponent=0.75): - """Vocabulary used by :class:`~gensim.models.doc2vec.Doc2Vec`. - - This includes a mapping from words found in the corpus to their total frequency count. - - Parameters - ---------- - max_vocab_size : int, optional - Maximum number of words in the Vocabulary. Used to limit the 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. - min_count : int - Words with frequency lower than this limit will be discarded from the vocabulary. - sample : float, optional - The threshold for configuring which higher-frequency words are randomly downsampled, - useful range is (0, 1e-5). - sorted_vocab : bool - If True, sort the vocabulary by descending frequency before assigning word indexes. - null_word : {0, 1} - If True, a null pseudo-word will be created for padding when using concatenative L1 (run-of-words). - This word is only ever input – never predicted – so count, huffman-point, etc doesn't matter. - ns_exponent : float, optional - The exponent used to shape the negative sampling distribution. A value of 1.0 samples exactly in proportion - to the frequencies, 0.0 samples all words equally, while a negative value samples low-frequency words more - than high-frequency words. The popular default value of 0.75 was chosen by the original Word2Vec paper. - More recently, in https://arxiv.org/abs/1804.04212, Caselles-Dupré, Lesaint, & Royo-Letelier suggest that - other values may perform better for recommendation applications. - - """ - super(Doc2VecVocab, self).__init__( - max_vocab_size=max_vocab_size, min_count=min_count, sample=sample, - sorted_vocab=sorted_vocab, null_word=null_word, ns_exponent=ns_exponent) - - def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): + def _scan_vocab(self, corpus_iterable, progress_per, trim_rule): document_no = -1 total_words = 0 min_reduce = 1 @@ -1015,7 +923,7 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): max_rawint = -1 # highest raw int tag seen (-1 for none) doctags_lookup = {} doctags_list = [] - for document_no, document in enumerate(documents): + for document_no, document in enumerate(corpus_iterable): if not checked_string_types: if isinstance(document.words, string_types): logger.warning( @@ -1028,7 +936,7 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): 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(docvecs) + document_no, total_words, interval_rate, len(vocab), len(doctags_list) ) interval_start = default_timer() interval_count = total_words @@ -1054,21 +962,26 @@ def _scan_vocab(self, documents, docvecs, progress_per, trim_rule): utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule) min_reduce += 1 + corpus_count = document_no + 1 + if len(doctags_list) > corpus_count: + logger.warning("More unique tags (%i) than documents (%i).", len(doctags_list), corpus_count) + if max_rawint > corpus_count: + logger.warning( + "Highest int doctag (%i) larger than count of documents (%i). This means " + "at least %i excess, unused slots (%i bytes) will be allocated for vectors.", + max_rawint, corpus_count, ((max_rawint - corpus_count) * self.vector_size * 4)) if max_rawint > -1: # adjust indexes/list to account for range of pure-int keyed doctags for key in doctags_list: doctags_lookup[key].index = doctags_lookup[key].index + max_rawint + 1 doctags_list = ConcatList([range(0, max_rawint + 1), doctags_list]) - docvecs.vocab = doctags_lookup - docvecs.index2key = doctags_list - corpus_count = document_no + 1 - if len(doctags_list) > corpus_count: - logger.warn("More unique tags (%i) than documents (%i).", len(doctags_list), corpus_count) + self.docvecs.map = doctags_lookup + self.docvecs.index2key = doctags_list self.raw_vocab = vocab return total_words, corpus_count - def scan_vocab(self, documents=None, corpus_file=None, docvecs=None, progress_per=10000, trim_rule=None): + def scan_vocab(self, corpus_iterable=None, corpus_file=None, docvecs=None, progress_per=10000, trim_rule=None): """Create the models Vocabulary: A mapping from unique words in the corpus to their frequency count. Parameters @@ -1105,49 +1018,54 @@ def scan_vocab(self, documents=None, corpus_file=None, docvecs=None, progress_pe """ logger.info("collecting all words and their counts") if corpus_file is not None: - documents = TaggedLineDocument(corpus_file) + corpus_iterable = TaggedLineDocument(corpus_file) - total_words, corpus_count = self._scan_vocab(documents, docvecs, progress_per, trim_rule) + total_words, corpus_count = self._scan_vocab(corpus_iterable, progress_per, trim_rule) logger.info( "collected %i word types and %i unique tags from a corpus of %i examples and %i words", - len(self.raw_vocab), len(docvecs), corpus_count, total_words + len(self.raw_vocab), len(self.docvecs), corpus_count, total_words ) return total_words, corpus_count + def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=None, steps=None): + """Compute cosine similarity between two post-bulk out of training documents. -class Doc2VecTrainables(Word2VecTrainables): - def __init__(self, dm=1, dm_concat=0, dm_tag_count=1, vector_size=100, seed=1, hashfxn=hash, window=5): - """Represents the inner shallow neural network used to train :class:`~gensim.models.doc2vec.Doc2Vec`.""" - super(Doc2VecTrainables, self).__init__( - vector_size=vector_size, seed=seed, hashfxn=hashfxn) - if dm and dm_concat: - self.layer1_size = (dm_tag_count + (2 * window)) * vector_size - logger.info("using concatenative %d-dimensional layer1", self.layer1_size) + 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. - def prepare_weights(self, hs, negative, wv, docvecs, update=False): - """Build tables and model weights based on final vocabulary settings.""" - # set initial input/projection and hidden weights - if not update: - self.reset_weights(hs, negative, wv, docvecs) - else: - self.update_weights(hs, negative, wv) - - def reset_weights(self, hs, negative, wv, docvecs, vocabulary=None): - super(Doc2VecTrainables, self).reset_weights(hs, negative, wv) - self.reset_doc_weights(docvecs) - - def reset_doc_weights(self, docvecs): - docvecs.resize_vectors() - docvecs.randomly_initialize_vectors() - if docvecs.mapfile_path: - self.vectors_docs_lockf = np_memmap( - docvecs.mapfile_path + '.vectors_docs_lockf', dtype=REAL, mode='w+', shape=(len(docvecs.vectors),) - ) - self.vectors_docs_lockf.fill(1.0) - else: - self.vectors_docs_lockf = ones((len(docvecs.vectors),), dtype=REAL) # zeros suppress learning + Returns + ------- + float + The cosine similarity between `doc_words1` and `doc_words2`. + + """ + d1 = self.infer_vector(doc_words=doc_words1, alpha=alpha, min_alpha=min_alpha, steps=steps) + d2 = self.infer_vector(doc_words=doc_words2, alpha=alpha, min_alpha=min_alpha, steps=steps) + return np.dot(matutils.unitvec(d1), matutils.unitvec(d2)) + + +class Doc2VecVocab(utils.SaveLoad): + """Obsolete class retained for now as load-compatibility state capture""" + pass + + +class Doc2VecTrainables(utils.SaveLoad): + """Obsolete class retained for now as load-compatibility state capture""" + pass class TaggedBrownCorpus(object): diff --git a/gensim/models/doc2vec_inner.pyx b/gensim/models/doc2vec_inner.pyx index 8d9ca4862f..e06aa00a35 100644 --- a/gensim/models/doc2vec_inner.pyx +++ b/gensim/models/doc2vec_inner.pyx @@ -225,14 +225,14 @@ cdef init_d2v_config(Doc2VecConfig *c, model, alpha, learn_doctags, learn_words, doctag_locks=None, docvecs_count=0): c[0].hs = model.hs c[0].negative = model.negative - c[0].sample = (model.vocabulary.sample != 0) + c[0].sample = (model.sample != 0) c[0].cbow_mean = model.cbow_mean c[0].train_words = train_words c[0].learn_doctags = learn_doctags c[0].learn_words = learn_words c[0].learn_hidden = learn_hidden c[0].alpha = alpha - c[0].layer1_size = model.trainables.layer1_size + c[0].layer1_size = model.layer1_size c[0].vector_size = model.docvecs.vector_size c[0].workers = model.workers c[0].docvecs_count = docvecs_count @@ -251,28 +251,28 @@ cdef init_d2v_config(Doc2VecConfig *c, model, alpha, learn_doctags, learn_words, doctag_vectors = model.docvecs.vectors_docs c[0].doctag_vectors = (np.PyArray_DATA(doctag_vectors)) if word_locks is None: - word_locks = model.trainables.vectors_lockf + word_locks = model.wv.vectors_lockf c[0].word_locks = (np.PyArray_DATA(word_locks)) if doctag_locks is None: - doctag_locks = model.trainables.vectors_docs_lockf + doctag_locks = model.docvecs.vectors_lockf c[0].doctag_locks = (np.PyArray_DATA(doctag_locks)) if c[0].hs: - c[0].syn1 = (np.PyArray_DATA(model.trainables.syn1)) + c[0].syn1 = (np.PyArray_DATA(model.syn1)) if c[0].negative: - c[0].syn1neg = (np.PyArray_DATA(model.trainables.syn1neg)) - c[0].cum_table = (np.PyArray_DATA(model.vocabulary.cum_table)) - c[0].cum_table_len = len(model.vocabulary.cum_table) + c[0].syn1neg = (np.PyArray_DATA(model.syn1neg)) + c[0].cum_table = (np.PyArray_DATA(model.cum_table)) + c[0].cum_table_len = len(model.cum_table) if c[0].negative or c[0].sample: c[0].next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24) # convert Python structures to primitive types, so we can release the GIL if work is None: - work = zeros(model.trainables.layer1_size, dtype=REAL) + work = zeros(model.layer1_size, dtype=REAL) c[0].work = np.PyArray_DATA(work) if neu1 is None: - neu1 = zeros(model.trainables.layer1_size, dtype=REAL) + neu1 = zeros(model.layer1_size, dtype=REAL) c[0].neu1 = np.PyArray_DATA(neu1) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index ae6ea0870a..bdc2cf9319 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # -# Authors: Shiva Manne , Chinmaya Pancholi +# Authors: Gensim Contributors # Copyright (C) 2018 RaRe Technologies s.r.o. # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html @@ -37,7 +37,7 @@ ['human', 'interface', 'computer'] >>> print(len(common_texts)) 9 - >>> model = FastText(size=4, window=3, min_count=1) # instantiate + >>> model = FastText(vector_size=4, window=3, min_count=1) # instantiate >>> model.build_vocab(sentences=common_texts) >>> model.train(sentences=common_texts, total_examples=len(common_texts), epochs=10) # train @@ -50,7 +50,7 @@ .. sourcecode:: pycon - >>> model2 = FastText(size=4, window=3, min_count=1, sentences=common_texts, iter=10) + >>> model2 = FastText(vector_size=4, window=3, min_count=1, sentences=common_texts, iter=10) .. Important:: This style of initialize-and-train in a single line is **deprecated**. We include it here @@ -84,7 +84,7 @@ >>> from gensim.test.utils import datapath >>> >>> corpus_file = datapath('lee_background.cor') # absolute path to corpus - >>> model3 = FastText(size=4, window=3, min_count=1) + >>> model3 = FastText(vector_size=4, window=3, min_count=1) >>> model3.build_vocab(corpus_file=corpus_file) # scan over corpus to build the vocabulary >>> >>> total_words = model3.corpus_total_words # number of words in the corpus @@ -116,7 +116,7 @@ ... yield list(tokenize(line)) >>> >>> - >>> model4 = FastText(size=4, window=3, min_count=1) + >>> model4 = FastText(vector_size=4, window=3, min_count=1) >>> model4.build_vocab(sentences=MyIter()) >>> total_examples = model4.corpus_count >>> model4.train(sentences=MyIter(), total_examples=total_examples, epochs=5) @@ -258,10 +258,7 @@ - :mod:`gensim.models.fasttext`: This module. Contains FastText-specific functionality only. - :mod:`gensim.models.keyedvectors`: Implements generic functionality. -- :mod:`gensim.models.word2vec`: Contains implementations for the vocabulary - and the trainables for FastText. -- :mod:`gensim.models.base_any2vec`: Contains implementations for the base. - classes, including functionality such as callbacks, logging. +- :mod:`gensim.models.word2vec`: Provides much of the basic scan & train framework. - :mod:`gensim.utils`: Implements model I/O (loading and saving). Our implementation relies heavily on inheritance. @@ -288,9 +285,8 @@ import gensim.models._fasttext_bin -from gensim.models.word2vec import Word2VecVocab, Word2VecTrainables +from gensim.models.word2vec import Word2Vec from gensim.models.keyedvectors import KeyedVectors -from gensim.models.base_any2vec import BaseWordEmbeddingsModel from gensim.utils import deprecated, call_on_class_only, open, NO_CYTHON @@ -312,10 +308,11 @@ raise NO_CYTHON -class FastText(BaseWordEmbeddingsModel): - def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, +class FastText(Word2Vec): + def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100, alpha=0.025, + window=5, min_count=5, max_vocab_size=None, word_ngrams=1, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, - negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, min_n=3, max_n=6, + negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, epochs=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, bucket=2000000, trim_rule=None, batch_words=MAX_WORDS_IN_BATCH, callbacks=(), compatible_hash=True): """Train, use and evaluate word representations learned using the method @@ -470,27 +467,62 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, size=100, alpha if self.word_ngrams <= 1 and max_n == 0: bucket = 0 - self.wv = FastTextKeyedVectors(size, min_n, max_n, bucket, compatible_hash) - self.vocabulary = FastTextVocab( - max_vocab_size=max_vocab_size, min_count=min_count, sample=sample, - sorted_vocab=bool(sorted_vocab), null_word=null_word, ns_exponent=ns_exponent) - self.trainables = FastTextTrainables(vector_size=size, seed=seed, bucket=bucket, hashfxn=hashfxn) - self.trainables.prepare_weights(hs, negative, self.wv, update=False, vocabulary=self.vocabulary) - self.wv.bucket = self.trainables.bucket + self.wv = FastTextKeyedVectors(vector_size, min_n, max_n, bucket, compatible_hash) + self.bucket = bucket + self.wv.bucket = bucket super(FastText, self).__init__( - sentences=sentences, corpus_file=corpus_file, workers=workers, vector_size=size, epochs=iter, + sentences=sentences, corpus_file=corpus_file, workers=workers, vector_size=vector_size, epochs=epochs, callbacks=callbacks, batch_words=batch_words, trim_rule=trim_rule, sg=sg, alpha=alpha, window=window, + max_vocab_size=max_vocab_size, min_count=min_count, sample=sample, sorted_vocab=sorted_vocab, + null_word=null_word, ns_exponent=ns_exponent, hashfxn=hashfxn, seed=seed, hs=hs, negative=negative, cbow_mean=cbow_mean, min_alpha=min_alpha) - def build_vocab(self, sentences=None, corpus_file=None, update=False, progress_per=10000, keep_raw_vocab=False, - trim_rule=None, **kwargs): + def prepare_weights(self, update=False): + """In addition to superclass allocations, compute ngrams of all words present in vocabulary. + + Parameters + ---------- + update : bool + If True, the new vocab words and their new ngrams word vectors are initialized + with random uniform distribution and updated/added to the existing vocab word and ngram vectors. + """ + super(FastText, self).prepare_weights(update=update) + if not update: + self.wv.init_ngrams_weights(self.seed) + self.wv.vectors_vocab_lockf = ones(len(self.wv.vectors_vocab), dtype=REAL) + self.wv.vectors_ngrams_lockf = ones(len(self.wv.vectors_ngrams), dtype=REAL) + else: + self.wv.update_ngrams_weights(self.seed, self.old_vocab_len) + self.wv.vectors_vocab_lockf = _pad_ones(self.wv.vectors_vocab_lockf, len(self.wv.vectors_vocab)) + self.wv.vectors_ngrams_lockf = _pad_ones(self.wv.vectors_ngrams_lockf, len(self.wv.vectors_ngrams)) + + def init_post_load(self, hidden_output): + num_vectors = len(self.wv.vectors) + vocab_size = len(self.wv.vocab) + vector_size = self.wv.vector_size + + assert num_vectors > 0, 'expected num_vectors to be initialized already' + assert vocab_size > 0, 'expected vocab_size to be initialized already' + + self.wv.vectors_ngrams_lockf = ones(len(self.wv.vectors_ngrams), dtype=REAL) + self.wv.vectors_vocab_lockf = ones(len(self.wv.vectors_vocab.shape), dtype=REAL) + + if self.hs: + self.syn1 = hidden_output + if self.negative: + self.syn1neg = hidden_output + + self.layer1_size = vector_size + + def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, progress_per=10000, + keep_raw_vocab=False, trim_rule=None, **kwargs): """Build vocabulary from a sequence of sentences (can be a once-only generator stream). Each sentence must be a list of unicode strings. Parameters ---------- - sentences : iterable of list of str, optional + corpus_iterable : iterable of list of str, optional Can be simply a list of lists of tokens, but for larger corpora, consider an iterable that streams the sentences directly from disk/network. See :class:`~gensim.models.word2vec.BrownCorpus`, :class:`~gensim.models.word2vec.Text8Corpus` @@ -521,7 +553,7 @@ def build_vocab(self, sentences=None, corpus_file=None, update=False, progress_p **kwargs Additional key word parameters passed to - :meth:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.build_vocab`. + :meth:`~gensim.models.word2vec.Word2Vec.build_vocab`. Examples -------- @@ -542,7 +574,7 @@ def build_vocab(self, sentences=None, corpus_file=None, update=False, progress_p """ if not update: - self.wv.init_ngrams_weights(self.trainables.seed) + self.wv.init_ngrams_weights(self.seed) elif not len(self.wv.vocab): raise RuntimeError( "You cannot do an online vocabulary-update of a model which has no prior vocabulary. " @@ -551,43 +583,30 @@ def build_vocab(self, sentences=None, corpus_file=None, update=False, progress_p "before doing an online update." ) else: - self.vocabulary.old_vocab_len = len(self.wv.vocab) + self.old_vocab_len = len(self.wv.vocab) retval = super(FastText, self).build_vocab( - sentences=sentences, corpus_file=corpus_file, update=update, progress_per=progress_per, + corpus_iterable=corpus_iterable, corpus_file=corpus_file, update=update, progress_per=progress_per, keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, **kwargs) if update: - self.wv.update_ngrams_weights(self.trainables.seed, self.vocabulary.old_vocab_len) + self.wv.update_ngrams_weights(self.seed, self.old_vocab_len) return retval - def _set_train_params(self, **kwargs): - # - # We need the wv.buckets_word member to be initialized in order to - # continue training. The _clear_post_train method destroys this - # variable, so we reinitialize it here, if needed. - # - # The .old_vocab_len member is set only to keep the init_ngrams_weights method happy. - # - if self.wv.buckets_word is None: - self.vocabulary.old_vocab_len = len(self.wv.vocab) - self.trainables.init_ngrams_weights(self.wv, update=True, vocabulary=self.vocabulary) - def _clear_post_train(self): """Clear the model's internal structures after training has finished to free up RAM.""" self.wv.vectors_norm = None - self.wv.buckets_word = None self.wv.adjust_vectors() # ensure composite-word vecs reflect latest training def estimate_memory(self, vocab_size=None, report=None): vocab_size = vocab_size or len(self.wv.vocab) vec_size = self.vector_size * np.dtype(np.float32).itemsize - l1_size = self.trainables.layer1_size * np.dtype(np.float32).itemsize + l1_size = self.layer1_size * np.dtype(np.float32).itemsize report = report or {} report['vocab'] = len(self.wv.vocab) * (700 if self.hs else 500) report['syn0_vocab'] = len(self.wv.vocab) * vec_size - num_buckets = self.trainables.bucket + num_buckets = self.bucket if self.hs: report['syn1'] = len(self.wv.vocab) * l1_size if self.negative: @@ -595,7 +614,7 @@ def estimate_memory(self, vocab_size=None, report=None): if self.word_ngrams > 0 and self.wv.vocab: num_buckets = num_ngrams = 0 - if self.trainables.bucket: + if self.bucket: buckets = set() num_ngrams = 0 for word in self.wv.vocab: @@ -603,7 +622,7 @@ def estimate_memory(self, vocab_size=None, report=None): word, self.wv.min_n, self.wv.max_n, - self.trainables.bucket, + self.bucket, self.wv.compatible_hash ) num_ngrams += len(hashes) @@ -669,7 +688,7 @@ def _do_train_job(self, sentences, alpha, inits): return tally, self._raw_word_count(sentences) - def train(self, sentences=None, corpus_file=None, total_examples=None, total_words=None, + def train(self, corpus_iterable=None, corpus_file=None, total_examples=None, total_words=None, epochs=None, start_alpha=None, end_alpha=None, word_count=0, queue_factor=2, report_delay=1.0, callbacks=(), **kwargs): """Update the model's neural weights from a sequence of sentences (can be a once-only generator stream). @@ -736,20 +755,26 @@ def train(self, sentences=None, corpus_file=None, total_examples=None, total_wor """ - if corpus_file is None and sentences is None: - raise TypeError("Either one of corpus_file or sentences value must be provided") + if corpus_file is None and corpus_iterable is None: + raise TypeError("Either one of corpus_file or corpus_iterable value must be provided") - if corpus_file is not None and sentences is not None: - raise TypeError("Both corpus_file and sentences must not be provided at the same time") + if corpus_file is not None and corpus_iterable is not None: + raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time") - if sentences is None and not os.path.isfile(corpus_file): + if corpus_iterable is None and not os.path.isfile(corpus_file): raise TypeError("Parameter corpus_file must be a valid path to a file, got %r instead" % corpus_file) - if sentences is not None and not isinstance(sentences, Iterable): - raise TypeError("sentences must be an iterable of list, got %r instead" % sentences) + if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable): + raise TypeError("sentences must be an iterable of list, got %r instead" % corpus_iterable) + + if self.wv.buckets_word is None: + logger.warn("self.wv.buckets_word was None; fixing.") + self.old_vocab_len = len(self.wv.vocab) + self.wv.init_ngrams_weights(seed=self.seed) super(FastText, self).train( - sentences=sentences, corpus_file=corpus_file, total_examples=total_examples, total_words=total_words, + corpus_iterable=corpus_iterable, corpus_file=corpus_file, + total_examples=total_examples, total_words=total_words, epochs=epochs, start_alpha=start_alpha, end_alpha=end_alpha, word_count=word_count, queue_factor=queue_factor, report_delay=report_delay, callbacks=callbacks) self.wv.adjust_vectors() @@ -767,8 +792,8 @@ def init_sims(self, replace=False): # init_sims() resides in KeyedVectors because it deals with input layer mainly, but because the # hidden layer is not an attribute of KeyedVectors, it has to be deleted in this class. # The normalizing of input layer happens inside of KeyedVectors. - if replace and hasattr(self.trainables, 'syn1'): - del self.trainables.syn1 + if replace and hasattr(self, 'syn1'): + del self.syn1 self.wv.init_sims(replace) def clear_sims(self): @@ -850,141 +875,36 @@ def load(cls, *args, **kwargs): Save :class:`~gensim.models.fasttext.FastText` model. """ - try: - model = super(FastText, cls).load(*args, **kwargs) - - if not hasattr(model.trainables, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'): - model.trainables.vectors_vocab_lockf = ones(len(model.wv.vectors_vocab), dtype=REAL) - if not hasattr(model.trainables, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'): - model.trainables.vectors_ngrams_lockf = ones(len(model.wv.vectors_ngrams), dtype=REAL) - - # fixup mistakenly overdimensioned gensim-3.x lockf arrays - if len(model.trainables.vectors_vocab_lockf.shape) > 1: - model.trainables.vectors_vocab_lockf = model.trainables.vectors_vocab_lockf[:, 0] - if len(model.trainables.vectors_ngrams_lockf.shape) > 1: - model.trainables.vectors_ngrams_lockf = model.trainables.vectors_ngrams_lockf[:, 0] - - if not hasattr(model.wv, 'bucket'): - model.wv.bucket = model.trainables.bucket - except AttributeError: - logger.info('Model saved using code from earlier Gensim Version. Re-loading old model in a compatible way.') - from gensim.models.deprecated.fasttext import load_old_fasttext - model = load_old_fasttext(*args, **kwargs) + model = super(FastText, cls).load(*args, rethrow=True, **kwargs) + + if not hasattr(model.wv, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'): + # TODO: try trainables-location + model.wv.vectors_vocab_lockf = ones(len(model.wv.vectors_vocab), dtype=REAL) + if not hasattr(model, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'): + # TODO: try trainables-location + model.wv.vectors_ngrams_lockf = ones(len(model.wv.vectors_ngrams), dtype=REAL) + # fixup mistakenly overdimensioned gensim-3.x lockf arrays + if len(model.wv.vectors_vocab_lockf.shape) > 1: + model.wv.vectors_vocab_lockf = model.wv.vectors_vocab_lockf[:, 0] + if len(model.wv.vectors_ngrams_lockf.shape) > 1: + model.wv.vectors_ngrams_lockf = model.wv.vectors_ngrams_lockf[:, 0] + if not hasattr(model, 'bucket'): + model.bucket = model.wv.bucket _try_upgrade(model.wv) return model -class FastTextVocab(Word2VecVocab): +class FastTextVocab(utils.SaveLoad): """This is a redundant class. It exists only to maintain backwards compatibility with older gensim versions.""" pass -class FastTextTrainables(Word2VecTrainables): - """Represents the inner shallow neural network used to train :class:`~gensim.models.fasttext.FastText`. - - Mostly inherits from its parent (:class:`~gensim.models.word2vec.Word2VecTrainables`). - Adds logic for calculating and maintaining ngram weights. - - Attributes - ---------- - hashfxn : function - Used for randomly initializing weights. Defaults to the built-in hash() - layer1_size : int - The size of the inner layer of the NN. Equal to the vector dimensionality. - Set in the :class:`~gensim.models.word2vec.Word2VecTrainables` constructor. - seed : float - The random generator seed used in reset_weights and update_weights. - syn1 : numpy.array - The inner layer of the NN. Each row corresponds to a term in the vocabulary. - Columns correspond to weights of the inner layer. - There are layer1_size such weights. - Set in the reset_weights and update_weights methods, only if hierarchical sampling is used. - syn1neg : numpy.array - Similar to syn1, but only set if negative sampling is used. - vectors_lockf : numpy.array - A one-dimensional array with one element for each term in the vocab. Set in reset_weights to an array of ones. - vectors_vocab_lockf : numpy.array - Similar to vectors_vocab_lockf, ones(len(model.trainables.vectors), dtype=REAL) - vectors_ngrams_lockf : numpy.array - np.ones((self.bucket, wv.vector_size), dtype=REAL) - - """ - def __init__(self, vector_size=100, seed=1, hashfxn=hash, bucket=2000000): - super(FastTextTrainables, self).__init__( - vector_size=vector_size, seed=seed, hashfxn=hashfxn) - self.bucket = int(bucket) - - # - # There are also two "hidden" attributes that get initialized outside - # this constructor: - # - # 1. vectors_vocab_lockf - # 2. vectors_ngrams_lockf - # - # These are both 1D matrices of shapes equal to the lengths of - # wv.vectors_vocab and wv.vectors_ngrams. So, each row corresponds to - # a vector. - # - # Lockf stands for "lock factor": zero values suppress learning, one - # values enable it. The vectors_vocab_lockf and vectors_ngrams_lockf - # are used only by the Cython code in fasttext_inner.pyx. - # - # The word2vec implementation also uses vectors_lockf: in that case, - # it's a 1D array, with a real number for each vector. The FastText - # implementation inherits this vectors_lockf attribute but doesn't - # appear to use it. - # - - def prepare_weights(self, hs, negative, wv, update=False, vocabulary=None): - super(FastTextTrainables, self).prepare_weights(hs, negative, wv, update=update, vocabulary=vocabulary) - self.init_ngrams_weights(wv, update=update, vocabulary=vocabulary) - - def init_ngrams_weights(self, wv, update=False, vocabulary=None): - """Compute ngrams of all words present in vocabulary and stores vectors for only those ngrams. - Vectors for other ngrams are initialized with a random uniform distribution in FastText. - - Parameters - ---------- - wv : :class:`~gensim.models.fasttext.FastTextKeyedVectors` - Contains the mapping between the words and embeddings. - The vectors for the computed ngrams will go here. - update : bool - If True, the new vocab words and their new ngrams word vectors are initialized - with random uniform distribution and updated/added to the existing vocab word and ngram vectors. - vocabulary : :class:`~gensim.models.fasttext.FastTextVocab` - This object represents the vocabulary of the model. - If update is True, then vocabulary may not be None. - - """ - if not update: - wv.init_ngrams_weights(self.seed) - self.vectors_vocab_lockf = ones(len(wv.vectors_vocab), dtype=REAL) - self.vectors_ngrams_lockf = ones(len(wv.vectors_ngrams), dtype=REAL) - else: - wv.update_ngrams_weights(self.seed, vocabulary.old_vocab_len) - self.vectors_vocab_lockf = _pad_ones(self.vectors_vocab_lockf, len(wv.vectors_vocab)) - self.vectors_ngrams_lockf = _pad_ones(self.vectors_ngrams_lockf, len(wv.vectors_ngrams)) - - def init_post_load(self, model, hidden_output): - num_vectors = len(model.wv.vectors) - vocab_size = len(model.wv.vocab) - vector_size = model.wv.vector_size - - assert num_vectors > 0, 'expected num_vectors to be initialized already' - assert vocab_size > 0, 'expected vocab_size to be initialized already' - - self.vectors_ngrams_lockf = ones(len(model.wv.vectors_ngrams), dtype=REAL) - self.vectors_vocab_lockf = ones(len(model.wv.vectors_vocab.shape), dtype=REAL) - - if model.hs: - self.syn1 = hidden_output - if model.negative: - self.syn1neg = hidden_output - - self.layer1_size = vector_size +class FastTextTrainables(utils.SaveLoad): + """Obsolete class retained for backward-compatible load()s""" + pass def _pad_ones(m, new_len): @@ -1113,8 +1033,8 @@ def load_facebook_vectors(path, encoding='utf-8'): model training. """ - model_wrapper = _load_fasttext_format(path, encoding=encoding, full_model=False) - return model_wrapper.wv + full_model = _load_fasttext_format(path, encoding=encoding, full_model=False) + return full_model.wv def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): @@ -1140,9 +1060,9 @@ def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): m = gensim.models._fasttext_bin.load(fin, encoding=encoding, full_model=full_model) model = FastText( - size=m.dim, + vector_size=m.dim, window=m.ws, - iter=m.epoch, + epochs=m.epoch, negative=m.neg, hs=int(m.loss == 1), sg=int(m.model == 2), @@ -1153,9 +1073,9 @@ def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): max_n=m.maxn, ) model.corpus_total_words = m.ntokens - model.vocabulary.raw_vocab = m.raw_vocab - model.vocabulary.nwords = m.nwords - model.vocabulary.vocab_size = m.vocab_size + model.raw_vocab = m.raw_vocab + model.nwords = m.nwords + model.vocab_size = m.vocab_size # # This is here to fix https://github.com/RaRe-Technologies/gensim/pull/2373. @@ -1169,15 +1089,13 @@ def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): # Native models trained _without_ pretrained vectors already contain the # trimmed raw_vocab, so this change does not affect them. # - model.vocabulary.prepare_vocab( - model.hs, model.negative, model.wv, - update=True, min_count=1, - ) + model.prepare_vocab(update=True, min_count=1) model.num_original_vectors = m.vectors_ngrams.shape[0] model.wv.init_post_load(m.vectors_ngrams) - model.trainables.init_post_load(model, m.hidden_output) + model.init_post_load(m.hidden_output) + _check_model(model) logger.info("loaded %s weight matrix for fastText model from %s", m.vectors_ngrams.shape, fin.name) @@ -1192,28 +1110,22 @@ def _check_model(m): 'mismatch between vector size in model params ({}) and model vectors ({})' .format(m.wv.vector_size, m.wv.vectors_ngrams) ) - - try: - syn1neg = m.trainables.syn1neg - except AttributeError: - syn1neg = None - - if syn1neg is not None: - assert m.wv.vector_size == m.trainables.syn1neg.shape[1], ( + if m.syn1neg is not None: + assert m.wv.vector_size == m.syn1neg.shape[1], ( 'mismatch between vector size in model params ({}) and trainables ({})' .format(m.wv.vector_size, m.wv.vectors_ngrams) ) - assert len(m.wv.vocab) == m.vocabulary.nwords, ( + assert len(m.wv.vocab) == m.nwords, ( 'mismatch between final vocab size ({} words), ' - 'and expected number of words ({} words)'.format(len(m.wv.vocab), m.vocabulary.nwords) + 'and expected number of words ({} words)'.format(len(m.wv.vocab), m.nwords) ) - if len(m.wv.vocab) != m.vocabulary.vocab_size: + if len(m.wv.vocab) != m.vocab_size: # expecting to log this warning only for pretrained french vector, wiki.fr logger.warning( "mismatch between final vocab size (%s words), and expected vocab size (%s words)", - len(m.wv.vocab), m.vocabulary.vocab_size + len(m.wv.vocab), m.vocab_size ) @@ -1524,7 +1436,6 @@ def init_post_load(self, fb_vectors): self.vectors_vocab = np.array(fb_vectors[:vocab_words, :]) self.vectors_ngrams = np.array(fb_vectors[vocab_words:, :]) self.buckets_word = None # This can get initialized later - self.adjust_vectors() # calculate composite full-word vectors def adjust_vectors(self): diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index c2794d7d11..0702729c90 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -454,26 +454,26 @@ cdef void init_ft_config(FastTextConfig *c, model, alpha, _work, _neu1): """ c.hs = model.hs c.negative = model.negative - c.sample = (model.vocabulary.sample != 0) + c.sample = (model.sample != 0) c.cbow_mean = model.cbow_mean c.window = model.window c.workers = model.workers c.syn0_vocab = (np.PyArray_DATA(model.wv.vectors_vocab)) - c.word_locks_vocab = (np.PyArray_DATA(model.trainables.vectors_vocab_lockf)) + c.word_locks_vocab = (np.PyArray_DATA(model.wv.vectors_vocab_lockf)) c.syn0_ngrams = (np.PyArray_DATA(model.wv.vectors_ngrams)) - c.word_locks_ngrams = (np.PyArray_DATA(model.trainables.vectors_ngrams_lockf)) + c.word_locks_ngrams = (np.PyArray_DATA(model.wv.vectors_ngrams_lockf)) c.alpha = alpha c.size = model.wv.vector_size if c.hs: - c.syn1 = (np.PyArray_DATA(model.trainables.syn1)) + c.syn1 = (np.PyArray_DATA(model.syn1)) if c.negative: - c.syn1neg = (np.PyArray_DATA(model.trainables.syn1neg)) - c.cum_table = (np.PyArray_DATA(model.vocabulary.cum_table)) - c.cum_table_len = len(model.vocabulary.cum_table) + c.syn1neg = (np.PyArray_DATA(model.syn1neg)) + c.cum_table = (np.PyArray_DATA(model.cum_table)) + c.cum_table_len = len(model.cum_table) if c.negative or c.sample: c.next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 841600d828..d7ba89ce8e 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -187,9 +187,6 @@ from six.moves import zip, range from scipy import stats -# For backwards compatibility, see https://github.com/RaRe-Technologies/gensim/issues/2201 -# -from gensim.models.deprecated.keyedvectors import EuclideanKeyedVectors # noqa logger = logging.getLogger(__name__) @@ -220,6 +217,11 @@ def _load_specials(self, *args, **kwargs): # fixup rename/consolidation into index2key of older index2word, index2entity if not hasattr(self, 'index2key'): self.index2key = self.__dict__.pop('index2word', self.__dict__.pop('index2word', None)) + # fixup rename into vectors of older syn0 + if not hasattr(self, 'vectors'): + self.vectors = self.__dict__.pop('syn0', None) + self.vectors_norm = None + self.vector_size = self.vectors.shape[1] # fixup rename of vocab into map if 'map' not in self.__dict__: self.map = self.__dict__.pop('vocab', None) @@ -1383,6 +1385,7 @@ def similarity_unseen_docs(self, *args, **kwargs): # to help 3.8.1 & older pickles load properly Word2VecKeyedVectors = KeyedVectors Doc2VecKeyedVectors = KeyedVectors +EuclideanKeyedVectors = KeyedVectors def _l2_norm(m, replace=False): diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index b6a6c8c2d6..5432059ec4 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- # -# Author: Shiva Manne +# Author: Gensim Contributors # Copyright (C) 2018 RaRe Technologies s.r.o. # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html @@ -129,13 +129,13 @@ from collections import defaultdict, namedtuple from dataclasses import dataclass from typing import List +from types import GeneratorType import threading import itertools -import warnings +import copy from gensim.utils import keep_vocab_item, call_on_class_only, deprecated from gensim.models.keyedvectors import KeyedVectors, pseudorandom_weak_vector -from gensim.models.base_any2vec import BaseWordEmbeddingsModel try: from queue import Queue, Empty @@ -145,6 +145,7 @@ from numpy import exp, dot, zeros, dtype, float32 as REAL,\ uint32, seterr, array, uint8, vstack, fromstring, sqrt,\ sum as np_sum, ones, logaddexp +import numpy as np from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc from six import iteritems, itervalues, string_types @@ -228,12 +229,12 @@ def score_cbow_pair(model, word, l1): return sum(lprob) -class Word2Vec(BaseWordEmbeddingsModel): - def __init__(self, sentences=None, corpus_file=None, size=100, alpha=0.025, window=5, min_count=5, +class Word2Vec(utils.SaveLoad): + def __init__(self, sentences=None, corpus_file=None, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, - sg=0, hs=0, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, + sg=0, hs=0, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, epochs=5, null_word=0, trim_rule=None, sorted_vocab=1, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False, callbacks=(), - max_final_vocab=None): + comment=None, max_final_vocab=None): """Train, use and evaluate neural networks described in https://code.google.com/p/word2vec/. Once you're finished training a model (=no more updates, only querying) @@ -262,7 +263,7 @@ def __init__(self, sentences=None, corpus_file=None, size=100, alpha=0.025, wind Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or `corpus_file` arguments need to be passed (or none of them, in that case, the model is left uninitialized). - size : int, optional + vector_size : int, optional Dimensionality of the word vectors. window : int, optional Maximum distance between the current and predicted word within a sentence. @@ -310,8 +311,8 @@ def __init__(self, sentences=None, corpus_file=None, size=100, alpha=0.025, wind useful range is (0, 1e-5). hashfxn : function, optional Hash function to use to randomly initialize weights, for increased training reproducibility. - iter : int, optional - Number of iterations (epochs) over the corpus. + epochs : int, optional + Number of iterations (epochs) over the corpus. (Formerly: `iter`) trim_rule : function, optional 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). @@ -342,48 +343,516 @@ def __init__(self, sentences=None, corpus_file=None, size=100, alpha=0.025, wind -------- Initialize and train a :class:`~gensim.models.word2vec.Word2Vec` model - .. sourcecode:: pycon + .. sourcecode:: pycon + + >>> from gensim.models import Word2Vec + >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] + >>> model = Word2Vec(sentences, min_count=1) + + Attributes + ---------- + wv : :class:`~gensim.models.keyedvectors.KeyedVectors` + This object essentially contains the mapping between words and embeddings. After training, it can be used + directly to query those embeddings in various ways. See the module level docstring for examples. + + """ + corpus_iterable = sentences + + self.vector_size = int(vector_size) + self.workers = int(workers) + self.epochs = epochs + self.train_count = 0 + self.total_train_time = 0 + self.batch_words = batch_words + + self.sg = int(sg) + self.alpha = float(alpha) + self.min_alpha = float(min_alpha) + + self.window = int(window) + self.random = np.random.RandomState(seed) + + self.hs = int(hs) + self.negative = int(negative) + self.ns_exponent = ns_exponent + self.cbow_mean = int(cbow_mean) + self.compute_loss = bool(compute_loss) + self.running_training_loss = 0 + self.min_alpha_yet_reached = float(alpha) + self.corpus_count = 0 + self.corpus_total_words = 0 + + self.max_final_vocab = max_final_vocab + self.max_vocab_size = max_vocab_size + self.min_count = min_count + self.sample = sample + self.sorted_vocab = sorted_vocab + self.null_word = null_word + self.cum_table = None # for negative sampling + self.raw_vocab = None + + if not hasattr(self, 'wv'): # set unless subclass already set (eg: FastText) + self.wv = KeyedVectors(vector_size) + + self.hashfxn = hashfxn + self.seed = seed + if not hasattr(self, 'layer1_size'): # set unless subclass already set (as for Doc2Vec dm_concat mode) + self.layer1_size = vector_size + + self.comment = comment + + self.load = call_on_class_only + + if corpus_iterable is not None or corpus_file is not None: + self.build_vocab_and_train(corpus_iterable=corpus_iterable, corpus_file=corpus_file, + trim_rule=trim_rule, callbacks=callbacks) + else: + if trim_rule is not None: + logger.warning( + "The rule, if given, is only used to prune vocabulary during build_vocab() " + "and is not stored as part of the model. Model initialized without sentences. " + "trim_rule provided, if any, will be ignored.") + if callbacks: + logger.warning( + "Callbacks are no longer retained by the model, so must be provided whenever " + "training is triggered, as in initialization with a corpus or calling `train()`. " + "The callbacks provided in this initialization without triggering train will " + "be ignored.") + + def build_vocab_and_train(self, corpus_iterable=None, corpus_file=None, trim_rule=None, callbacks=None): + if not (corpus_iterable is None) ^ (corpus_file is None): + raise ValueError("You must provide only one of corpus_iterable or corpus_file arguments.") + if corpus_file is not None and not isinstance(corpus_file, string_types): + raise TypeError("You must pass string as the corpus_file argument.") + elif isinstance(corpus_iterable, GeneratorType): + raise TypeError("You can't pass a generator as the sentences argument. Try a sequence.") + # TODO: test for restartable? + self.build_vocab(corpus_iterable=corpus_iterable, corpus_file=corpus_file, trim_rule=trim_rule) + self.train( + corpus_iterable=corpus_iterable, corpus_file=corpus_file, total_examples=self.corpus_count, + total_words=self.corpus_total_words, epochs=self.epochs, start_alpha=self.alpha, + end_alpha=self.min_alpha, compute_loss=self.compute_loss, callbacks=callbacks) + + def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, progress_per=10000, + keep_raw_vocab=False, trim_rule=None, **kwargs): + """Build vocabulary from a sequence of sentences (can be a once-only generator stream). + + Parameters + ---------- + corpus_iterable : iterable of list of str + Can be simply a list of lists of tokens, but for larger corpora, + consider an iterable 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` module for such examples. + corpus_file : str, optional + Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. + You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or + `corpus_file` arguments need to be passed (not both of them). + update : bool + If true, the new words in `sentences` will be added to model's vocab. + progress_per : int, optional + Indicates how many words to process before showing/updating the progress. + keep_raw_vocab : bool, optional + If False, the raw vocabulary will be deleted after the scaling is done to free up RAM. + trim_rule : function, optional + 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, look to :func:`~gensim.utils.keep_vocab_item`), + or a callable that accepts parameters (word, count, min_count) and returns either + :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`. + The rule, if given, is only used to prune vocabulary during current method call and is not stored as part + of the model. + + The input parameters are of the following types: + * `word` (str) - the word we are examining + * `count` (int) - the word's frequency count in the corpus + * `min_count` (int) - the minimum count threshold. + + **kwargs : object + Key word arguments propagated to `self.prepare_vocab` + + """ + total_words, corpus_count = self.scan_vocab( + corpus_iterable=corpus_iterable, corpus_file=corpus_file, progress_per=progress_per, trim_rule=trim_rule) + self.corpus_count = corpus_count + self.corpus_total_words = total_words + report_values = self.prepare_vocab(update=update, keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, **kwargs) + report_values['memory'] = self.estimate_memory(vocab_size=report_values['num_retained_words']) + self.prepare_weights(update=update) + + def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=None, trim_rule=None, update=False): + """Build vocabulary from a dictionary of word frequencies. + + Parameters + ---------- + word_freq : dict of (str, int) + A mapping from a word in the vocabulary to its frequency count. + keep_raw_vocab : bool, optional + If False, delete the raw vocabulary after the scaling is done to free up RAM. + corpus_count : int, optional + Even if no corpus is provided, this argument can set corpus_count explicitly. + trim_rule : function, optional + 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, look to :func:`~gensim.utils.keep_vocab_item`), + or a callable that accepts parameters (word, count, min_count) and returns either + :attr:`gensim.utils.RULE_DISCARD`, :attr:`gensim.utils.RULE_KEEP` or :attr:`gensim.utils.RULE_DEFAULT`. + The rule, if given, is only used to prune vocabulary during current method call and is not stored as part + of the model. + + The input parameters are of the following types: + * `word` (str) - the word we are examining + * `count` (int) - the word's frequency count in the corpus + * `min_count` (int) - the minimum count threshold. + + update : bool, optional + If true, the new provided words in `word_freq` dict will be added to model's vocab. + + """ + logger.info("Processing provided word frequencies") + # Instead of scanning text, this will assign provided word frequencies dictionary(word_freq) + # to be directly the raw vocab + raw_vocab = word_freq + logger.info( + "collected %i different raw word, with total frequency of %i", + len(raw_vocab), sum(itervalues(raw_vocab)) + ) + + # Since no sentences are provided, this is to control the corpus_count. + self.corpus_count = corpus_count or 0 + self.raw_vocab = raw_vocab + + # trim by min_count & precalculate downsampling + report_values = self.prepare_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update) + report_values['memory'] = self.estimate_memory(vocab_size=report_values['num_retained_words']) + self.prepare_weights(update=update) # build tables & arrays + + def _scan_vocab(self, sentences, progress_per, trim_rule): + sentence_no = -1 + total_words = 0 + min_reduce = 1 + vocab = defaultdict(int) + checked_string_types = 0 + for sentence_no, sentence in enumerate(sentences): + if not checked_string_types: + if isinstance(sentence, string_types): + logger.warning( + "Each 'sentences' item should be a list of words (usually unicode strings). " + "First item here is instead plain %s.", + type(sentence) + ) + checked_string_types += 1 + if sentence_no % progress_per == 0: + logger.info( + "PROGRESS: at sentence #%i, processed %i words, keeping %i word types", + sentence_no, total_words, len(vocab) + ) + for word in sentence: + vocab[word] += 1 + total_words += len(sentence) + + 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 + + corpus_count = sentence_no + 1 + self.raw_vocab = vocab + return total_words, corpus_count + + def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=10000, workers=None, trim_rule=None): + logger.info("collecting all words and their counts") + if corpus_file: + corpus_iterable = LineSentence(corpus_file) + + total_words, corpus_count = self._scan_vocab(corpus_iterable, progress_per, trim_rule) + + logger.info( + "collected %i word types from a corpus of %i raw words and %i sentences", + len(self.raw_vocab), total_words, corpus_count + ) + + return total_words, corpus_count + + def sort_vocab(self): + """Sort the vocabulary so the most frequent words have the lowest indexes.""" + if len(self.wv.vectors): + raise RuntimeError("cannot sort vocabulary after model weights already initialized.") + self.wv.index2key.sort(key=lambda word: self.wv.vocab[word].count, reverse=True) + for i, word in enumerate(self.wv.index2key): + self.wv.vocab[word].index = i + + def prepare_vocab( + self, update=False, keep_raw_vocab=False, trim_rule=None, + min_count=None, sample=None, dry_run=False): + """Apply vocabulary settings for `min_count` (discarding less-frequent words) + and `sample` (controlling the downsampling of more-frequent words). + + Calling with `dry_run=True` will only simulate the provided settings and + report the size of the retained vocabulary, effective corpus length, and + estimated memory requirements. Results are both printed via logging and + returned as a dict. + + Delete the raw vocabulary after the scaling is done to free up RAM, + unless `keep_raw_vocab` is set. + + """ + min_count = min_count or self.min_count + sample = sample or self.sample + drop_total = drop_unique = 0 + + # set effective_min_count to min_count in case max_final_vocab isn't set + self.effective_min_count = min_count + + # if max_final_vocab is specified instead of min_count + # pick a min_count which satisfies max_final_vocab as well as possible + if self.max_final_vocab is not None: + sorted_vocab = sorted(self.raw_vocab.keys(), key=lambda word: self.raw_vocab[word], reverse=True) + calc_min_count = 1 + + if self.max_final_vocab < len(sorted_vocab): + calc_min_count = self.raw_vocab[sorted_vocab[self.max_final_vocab]] + 1 + + self.effective_min_count = max(calc_min_count, min_count) + logger.info( + "max_final_vocab=%d and min_count=%d resulted in calc_min_count=%d, effective_min_count=%d", + self.max_final_vocab, min_count, calc_min_count, self.effective_min_count + ) + + if not update: + logger.info("Loading a fresh vocabulary") + retain_total, retain_words = 0, [] + # Discard words less-frequent than min_count + if not dry_run: + self.wv.index2key = [] + # make stored settings match these applied settings + self.min_count = min_count + self.sample = sample + self.wv.vocab = {} + + for word, v in iteritems(self.raw_vocab): + if keep_vocab_item(word, v, self.effective_min_count, trim_rule=trim_rule): + retain_words.append(word) + retain_total += v + if not dry_run: + self.wv.vocab[word] = W2VVocab(count=v, index=len(self.wv.index2key)) + self.wv.index2key.append(word) + else: + drop_unique += 1 + drop_total += v + original_unique_total = len(retain_words) + drop_unique + retain_unique_pct = len(retain_words) * 100 / max(original_unique_total, 1) + logger.info( + "effective_min_count=%d retains %i unique words (%i%% of original %i, drops %i)", + self.effective_min_count, len(retain_words), retain_unique_pct, original_unique_total, drop_unique + ) + original_total = retain_total + drop_total + retain_pct = retain_total * 100 / max(original_total, 1) + logger.info( + "effective_min_count=%d leaves %i word corpus (%i%% of original %i, drops %i)", + self.effective_min_count, retain_total, retain_pct, original_total, drop_total + ) + else: + logger.info("Updating model with new vocabulary") + new_total = pre_exist_total = 0 + new_words = pre_exist_words = [] + for word, v in iteritems(self.raw_vocab): + if keep_vocab_item(word, v, self.effective_min_count, trim_rule=trim_rule): + if word in self.wv.vocab: + pre_exist_words.append(word) + pre_exist_total += v + if not dry_run: + self.wv.vocab[word].count += v + else: + new_words.append(word) + new_total += v + if not dry_run: + self.wv.vocab[word] = W2VVocab(count=v, index=len(self.wv.index2key)) + self.wv.index2key.append(word) + else: + drop_unique += 1 + drop_total += v + original_unique_total = len(pre_exist_words) + len(new_words) + drop_unique + pre_exist_unique_pct = len(pre_exist_words) * 100 / max(original_unique_total, 1) + new_unique_pct = len(new_words) * 100 / max(original_unique_total, 1) + logger.info( + "New added %i unique words (%i%% of original %i) " + "and increased the count of %i pre-existing words (%i%% of original %i)", + len(new_words), new_unique_pct, original_unique_total, len(pre_exist_words), + pre_exist_unique_pct, original_unique_total + ) + retain_words = new_words + pre_exist_words + retain_total = new_total + pre_exist_total + + # Precalculate each vocabulary item's threshold for sampling + if not sample: + # no words downsampled + threshold_count = retain_total + elif sample < 1.0: + # traditional meaning: set parameter as proportion of total + threshold_count = sample * retain_total + else: + # new shorthand: sample >= 1 means downsample all words with higher count than sample + threshold_count = int(sample * (3 + sqrt(5)) / 2) + + downsample_total, downsample_unique = 0, 0 + for w in retain_words: + v = self.raw_vocab[w] + word_probability = (sqrt(v / threshold_count) + 1) * (threshold_count / v) + if word_probability < 1.0: + downsample_unique += 1 + downsample_total += word_probability * v + else: + word_probability = 1.0 + downsample_total += v + if not dry_run: + self.wv.vocab[w].sample_int = int(round(word_probability * 2**32)) + + if not dry_run and not keep_raw_vocab: + logger.info("deleting the raw counts dictionary of %i items", len(self.raw_vocab)) + self.raw_vocab = defaultdict(int) + + logger.info("sample=%g downsamples %i most-common words", sample, downsample_unique) + logger.info( + "downsampling leaves estimated %i word corpus (%.1f%% of prior %i)", + downsample_total, downsample_total * 100.0 / max(retain_total, 1), retain_total + ) + + # return from each step: words-affected, resulting-corpus-size, extra memory estimates + report_values = { + 'drop_unique': drop_unique, 'retain_total': retain_total, 'downsample_unique': downsample_unique, + 'downsample_total': int(downsample_total), 'num_retained_words': len(retain_words) + } + + if self.null_word: + # create null pseudo-word for padding when using concatenative L1 (run-of-words) + # this word is only ever input – never predicted – so count, huffman-point, etc doesn't matter + self.add_null_word() + + if self.sorted_vocab and not update: + self.sort_vocab() + if self.hs: + # add info about each word's Huffman encoding + self.create_binary_tree() + if self.negative: + # build the table for drawing random words (for negative sampling) + self.make_cum_table() + + return report_values + + def estimate_memory(self, vocab_size=None, report=None): + """Estimate required memory for a model using current settings and provided vocabulary size. + + Parameters + ---------- + vocab_size : int, optional + Number of unique tokens in the vocabulary + report : dict of (str, int), optional + A dictionary from string representations of the model's memory consuming members to their size in bytes. + + Returns + ------- + dict of (str, int) + A dictionary from string representations of the model's memory consuming members to their size in bytes. + + """ + vocab_size = vocab_size or len(self.wv.vocab) + report = report or {} + report['vocab'] = vocab_size * (700 if self.hs else 500) + report['vectors'] = vocab_size * self.vector_size * dtype(REAL).itemsize + if self.hs: + report['syn1'] = vocab_size * self.layer1_size * dtype(REAL).itemsize + if self.negative: + report['syn1neg'] = vocab_size * self.layer1_size * dtype(REAL).itemsize + report['total'] = sum(report.values()) + logger.info( + "estimated required memory for %i words and %i dimensions: %i bytes", + vocab_size, self.vector_size, report['total'] + ) + return report + + def add_null_word(self): + word, v = '\0', W2VVocab(count=1, sample_int=0) + v.index = len(self.wv.vocab) + self.wv.index2key.append(word) + self.wv.vocab[word] = v + + def create_binary_tree(self): + """Create a `binary Huffman tree `_ using stored vocabulary + word counts. Frequent words will have shorter binary codes. + Called internally from :meth:`~gensim.models.word2vec.Word2VecVocab.build_vocab`. + + """ + _assign_binary_codes(self.wv.vocab) + + def make_cum_table(self, domain=2**31 - 1): + """Create a cumulative-distribution table using stored vocabulary word counts for + drawing random words in the negative-sampling training routines. + + To draw a word index, choose a random integer up to the maximum value in the table (cum_table[-1]), + then finding that integer's sorted insertion point (as if by `bisect_left` or `ndarray.searchsorted()`). + That insertion point is the drawn index, coming up in proportion equal to the increment at that slot. - >>> from gensim.models import Word2Vec - >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] - >>> model = Word2Vec(sentences, min_count=1) + """ + vocab_size = len(self.wv.index2key) + self.cum_table = zeros(vocab_size, dtype=uint32) + # compute sum of all power (Z in paper) + train_words_pow = 0.0 + for word_index in range(vocab_size): + train_words_pow += self.wv.vocab[self.wv.index2key[word_index]].count**self.ns_exponent + cumulative = 0.0 + for word_index in range(vocab_size): + cumulative += self.wv.vocab[self.wv.index2key[word_index]].count**self.ns_exponent + self.cum_table[word_index] = round(cumulative / train_words_pow * domain) + if len(self.cum_table) > 0: + assert self.cum_table[-1] == domain - Some important attributes are the following: + def prepare_weights(self, update=False): + """Build tables and model weights based on final vocabulary settings.""" + # set initial input/projection and hidden weights + if not update: + self.reset_weights() + else: + self.update_weights() - Attributes - ---------- - wv : :class:`~gensim.models.keyedvectors.KeyedVectors` - This object essentially contains the mapping between words and embeddings. After training, it can be used - directly to query those embeddings in various ways. See the module level docstring for examples. + @deprecated("Use gensim.models.keyedvectors.pseudorandom_weak_vector() directly") + def seeded_vector(self, seed_string, vector_size): + return pseudorandom_weak_vector(vector_size, seed_string=seed_string, hashfxn=self.hashfxn) - vocabulary : :class:`~gensim.models.word2vec.Word2VecVocab` - This object represents the vocabulary (sometimes called Dictionary in gensim) of the model. - Besides keeping track of all unique words, this object provides extra functionality, such as - constructing a huffman tree (frequent words are closer to the root), or discarding extremely rare words. + def reset_weights(self): + """Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary.""" + logger.info("resetting layer weights") + self.wv.resize_vectors() + self.wv.randomly_initialize_vectors(seed=self.seed) + if self.hs: + self.syn1 = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL) + if self.negative: + self.syn1neg = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL) - trainables : :class:`~gensim.models.word2vec.Word2VecTrainables` - This object represents the inner shallow neural network used to train the embeddings. The semantics - of the network differ slightly in the two available training modes (CBOW or SG) but you can think of it - as a NN with single projection and hidden layer which we train on the corpus. The weights are then used - as our embeddings (which means that the size of the hidden layer is equal to the number of features - `self.size`). + self.wv.vectors_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning - """ - self.max_final_vocab = max_final_vocab + def update_weights(self): + """Copy all the existing weights, and reset the weights for the newly added vocabulary.""" + logger.info("updating layer weights") + new_range = self.wv.resize_vectors() + gained_vocab = len(new_range) + self.wv.randomly_initialize_vectors(indexes=new_range) - self.callbacks = callbacks - self.load = call_on_class_only + # Raise an error if an online update is run before initial training on a corpus + if not len(self.wv.vectors): + raise RuntimeError( + "You cannot do an online vocabulary-update of a model which has no prior vocabulary. " + "First build the vocabulary of your model with a corpus before doing an online update." + ) - self.wv = KeyedVectors(size) - self.vocabulary = Word2VecVocab( - max_vocab_size=max_vocab_size, min_count=min_count, sample=sample, sorted_vocab=bool(sorted_vocab), - null_word=null_word, max_final_vocab=max_final_vocab, ns_exponent=ns_exponent) - self.trainables = Word2VecTrainables(seed=seed, vector_size=size, hashfxn=hashfxn) + if self.hs: + self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=REAL)]) + if self.negative: + pad = zeros((gained_vocab, self.layer1_size), dtype=REAL) + self.syn1neg = vstack([self.syn1neg, pad]) + self.wv.vectors_norm = None - super(Word2Vec, self).__init__( - sentences=sentences, corpus_file=corpus_file, workers=workers, vector_size=size, epochs=iter, - callbacks=callbacks, batch_words=batch_words, trim_rule=trim_rule, sg=sg, alpha=alpha, window=window, - seed=seed, hs=hs, negative=negative, cbow_mean=cbow_mean, min_alpha=min_alpha, compute_loss=compute_loss) + # do not suppress learning for already learned words + self.wv.vectors_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, total_examples=None, total_words=None, **kwargs): @@ -428,14 +897,10 @@ def _clear_post_train(self): """Remove all L2-normalized word vectors from the model.""" self.wv.vectors_norm = None - def _set_train_params(self, **kwargs): - if 'compute_loss' in kwargs: - self.compute_loss = kwargs['compute_loss'] - self.running_training_loss = 0 - - def train(self, sentences=None, corpus_file=None, total_examples=None, total_words=None, + def train(self, corpus_iterable=None, corpus_file=None, total_examples=None, total_words=None, epochs=None, start_alpha=None, end_alpha=None, word_count=0, - queue_factor=2, report_delay=1.0, compute_loss=False, callbacks=()): + queue_factor=2, report_delay=1.0, compute_loss=False, callbacks=(), + **kwargs): """Update the model's neural weights from a sequence of sentences. Notes @@ -454,63 +919,699 @@ def train(self, sentences=None, corpus_file=None, total_examples=None, total_wor Parameters ---------- - sentences : iterable of list of str - The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora, - consider an iterable 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` in :mod:`~gensim.models.word2vec` module for such examples. - See also the `tutorial on data streaming in Python - `_. - corpus_file : str, optional - Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. - You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or - `corpus_file` arguments need to be passed (not both of them). + corpus_iterable : iterable of list of str + The `sentences` iterable can be simply a list of lists of tokens, but for larger corpora, + consider an iterable 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` in :mod:`~gensim.models.word2vec` module for such examples. + See also the `tutorial on data streaming in Python + `_. + corpus_file : str, optional + Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. + You may use this argument instead of `sentences` to get performance boost. Only one of `sentences` or + `corpus_file` arguments need to be passed (not both of them). + total_examples : int + Count of sentences. + total_words : int + Count of raw words in sentences. + epochs : int + Number of iterations (epochs) over the corpus. + start_alpha : float, optional + Initial learning rate. If supplied, replaces the starting `alpha` from the constructor, + for this one call to`train()`. + Use only if making multiple calls to `train()`, when you want to manage the alpha learning-rate yourself + (not recommended). + end_alpha : float, optional + Final learning rate. Drops linearly from `start_alpha`. + If supplied, this replaces the final `min_alpha` from the constructor, for this one call to `train()`. + Use only if making multiple calls to `train()`, when you want to manage the alpha learning-rate yourself + (not recommended). + word_count : int, optional + Count of words already trained. Set this to 0 for the usual + case of training on all words in sentences. + queue_factor : int, optional + Multiplier for size of queue (number of workers * queue_factor). + report_delay : float, optional + Seconds to wait before reporting progress. + compute_loss: bool, optional + If True, computes and stores loss value which can be retrieved using + :meth:`~gensim.models.word2vec.Word2Vec.get_latest_training_loss`. + callbacks : iterable of :class:`~gensim.models.callbacks.CallbackAny2Vec`, optional + Sequence of callbacks to be executed at specific stages during training. + + Examples + -------- + .. sourcecode:: pycon + + >>> from gensim.models import Word2Vec + >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] + >>> + >>> model = Word2Vec(min_count=1) + >>> model.build_vocab(sentences) # prepare the model vocabulary + >>> model.train(sentences, total_examples=model.corpus_count, epochs=model.iter) # train word vectors + (1, 30) + + """ + self.alpha = start_alpha or self.alpha + self.min_alpha = end_alpha or self.min_alpha + self.epochs = epochs + + self._check_training_sanity( + epochs=epochs, + total_examples=total_examples, + total_words=total_words) + + self.compute_loss = compute_loss + self.running_training_loss = 0.0 + + for callback in callbacks: + callback.on_train_begin(self) + + trained_word_count = 0 + raw_word_count = 0 + start = default_timer() - 0.00001 + job_tally = 0 + + for cur_epoch in range(self.epochs): + for callback in callbacks: + callback.on_epoch_begin(self) + + if corpus_iterable is not None: + trained_word_count_epoch, raw_word_count_epoch, job_tally_epoch = self._train_epoch( + corpus_iterable, cur_epoch=cur_epoch, total_examples=total_examples, + total_words=total_words, queue_factor=queue_factor, report_delay=report_delay, + callbacks=callbacks, **kwargs) + else: + trained_word_count_epoch, raw_word_count_epoch, job_tally_epoch = self._train_epoch_corpusfile( + corpus_file, cur_epoch=cur_epoch, total_examples=total_examples, total_words=total_words, + callbacks=callbacks, **kwargs) + + trained_word_count += trained_word_count_epoch + raw_word_count += raw_word_count_epoch + job_tally += job_tally_epoch + + for callback in callbacks: + callback.on_epoch_end(self) + + # Log overall time + total_elapsed = default_timer() - start + self._log_train_end(raw_word_count, trained_word_count, total_elapsed, job_tally) + + self.train_count += 1 # number of times train() has been called + self._clear_post_train() + + for callback in callbacks: + callback.on_train_end(self) + return trained_word_count, raw_word_count + + def _worker_loop_corpusfile(self, corpus_file, thread_id, offset, cython_vocab, progress_queue, cur_epoch=0, + total_examples=None, total_words=None, **kwargs): + """Train the model on a `corpus_file` in LineSentence format. + + This function will be called in parallel by multiple workers (threads or processes) to make + optimal use of multicore machines. + + Parameters + ---------- + corpus_file : str + Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. + thread_id : int + Thread index starting from 0 to `number of workers - 1`. + offset : int + Offset (in bytes) in the `corpus_file` for particular worker. + cython_vocab : :class:`~gensim.models.word2vec_inner.CythonVocab` + Copy of the vocabulary in order to access it without GIL. + progress_queue : Queue of (int, int, int) + A queue of progress reports. Each report is represented as a tuple of these 3 elements: + * Size of data chunk processed, for example number of sentences in the corpus chunk. + * Effective word count used in training (after ignoring unknown words and trimming the sentence length). + * Total word count used in training. + **kwargs : object + Additional key word parameters for the specific model inheriting from this class. + + """ + thread_private_mem = self._get_thread_working_mem() + + examples, tally, raw_tally = self._do_train_epoch( + corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, + total_examples=total_examples, total_words=total_words, **kwargs) + + progress_queue.put((examples, tally, raw_tally)) + progress_queue.put(None) + + def _worker_loop(self, job_queue, progress_queue): + """Train the model, lifting batches of data from the queue. + + This function will be called in parallel by multiple workers (threads or processes) to make + optimal use of multicore machines. + + Parameters + ---------- + job_queue : Queue of (list of objects, (str, int)) + A queue of jobs still to be processed. The worker will take up jobs from this queue. + Each job is represented by a tuple where the first element is the corpus chunk to be processed and + the second is the dictionary of parameters. + progress_queue : Queue of (int, int, int) + A queue of progress reports. Each report is represented as a tuple of these 3 elements: + * Size of data chunk processed, for example number of sentences in the corpus chunk. + * Effective word count used in training (after ignoring unknown words and trimming the sentence length). + * Total word count used in training. + + """ + thread_private_mem = self._get_thread_working_mem() + jobs_processed = 0 + callbacks = progress_queue.callbacks + while True: + job = job_queue.get() + if job is None: + progress_queue.put(None) + break # no more jobs => quit this worker + data_iterable, job_parameters = job + + for callback in callbacks: + callback.on_batch_begin(self) + + tally, raw_tally = self._do_train_job(data_iterable, job_parameters, thread_private_mem) + + for callback in callbacks: + callback.on_batch_end(self) + + progress_queue.put((len(data_iterable), tally, raw_tally)) # report back progress + jobs_processed += 1 + logger.debug("worker exiting, processed %i jobs", jobs_processed) + + def _job_producer(self, data_iterator, job_queue, cur_epoch=0, total_examples=None, total_words=None): + """Fill the jobs queue using the data found in the input stream. + + Each job is represented by a tuple where the first element is the corpus chunk to be processed and + the second is a dictionary of parameters. + + Parameters + ---------- + data_iterator : iterable of list of objects + The input dataset. This will be split in chunks and these chunks will be pushed to the queue. + job_queue : Queue of (list of object, dict of (str, int)) + A queue of jobs still to be processed. The worker will take up jobs from this queue. + Each job is represented by a tuple where the first element is the corpus chunk to be processed and + the second is the dictionary of parameters. + cur_epoch : int, optional + The current training epoch, needed to compute the training parameters for each job. + For example in many implementations the learning rate would be dropping with the number of epochs. + total_examples : int, optional + Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences + in a corpus. Used to log progress. + total_words : int, optional + Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw + words in a corpus. Used to log progress. + + """ + job_batch, batch_size = [], 0 + pushed_words, pushed_examples = 0, 0 + next_job_params = self._get_job_params(cur_epoch) + job_no = 0 + + for data_idx, data in enumerate(data_iterator): + data_length = self._raw_word_count([data]) + + # can we fit this sentence into the existing job batch? + if batch_size + data_length <= self.batch_words: + # yes => add it to the current job + job_batch.append(data) + batch_size += data_length + else: + job_no += 1 + job_queue.put((job_batch, next_job_params)) + + # update the learning rate for the next job + if total_examples: + # examples-based decay + pushed_examples += len(job_batch) + epoch_progress = 1.0 * pushed_examples / total_examples + else: + # words-based decay + pushed_words += self._raw_word_count(job_batch) + epoch_progress = 1.0 * pushed_words / total_words + next_job_params = self._update_job_params(next_job_params, epoch_progress, cur_epoch) + + # add the sentence that didn't fit as the first item of a new job + job_batch, batch_size = [data], data_length + # add the last job too (may be significantly smaller than batch_words) + if job_batch: + job_no += 1 + job_queue.put((job_batch, next_job_params)) + + if job_no == 0 and self.train_count == 0: + logger.warning( + "train() called with an empty iterator (if not intended, " + "be sure to provide a corpus that offers restartable iteration = an iterable)." + ) + + # give the workers heads up that they can finish -- no more work! + for _ in range(self.workers): + job_queue.put(None) + logger.debug("job loop exiting, total %i jobs", job_no) + + def _log_epoch_progress(self, progress_queue=None, job_queue=None, cur_epoch=0, total_examples=None, + total_words=None, report_delay=1.0, is_corpus_file_mode=None): + """Get the progress report for a single training epoch. + + Parameters + ---------- + progress_queue : Queue of (int, int, int) + A queue of progress reports. Each report is represented as a tuple of these 3 elements: + * size of data chunk processed, for example number of sentences in the corpus chunk. + * Effective word count used in training (after ignoring unknown words and trimming the sentence length). + * Total word count used in training. + job_queue : Queue of (list of object, dict of (str, int)) + A queue of jobs still to be processed. The worker will take up jobs from this queue. + Each job is represented by a tuple where the first element is the corpus chunk to be processed and + the second is the dictionary of parameters. + cur_epoch : int, optional + The current training epoch, needed to compute the training parameters for each job. + For example in many implementations the learning rate would be dropping with the number of epochs. + total_examples : int, optional + Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences + in a corpus. Used to log progress. + total_words : int, optional + Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw + words in a corpus. Used to log progress. + report_delay : float, optional + Number of seconds between two consecutive progress report messages in the logger. + is_corpus_file_mode : bool, optional + Whether training is file-based (corpus_file argument) or not. + + Returns + ------- + (int, int, int) + The epoch report consisting of three elements: + * size of data chunk processed, for example number of sentences in the corpus chunk. + * Effective word count used in training (after ignoring unknown words and trimming the sentence length). + * Total word count used in training. + + """ + example_count, trained_word_count, raw_word_count = 0, 0, 0 + start, next_report = default_timer() - 0.00001, 1.0 + job_tally = 0 + unfinished_worker_count = self.workers + + while unfinished_worker_count > 0: + report = progress_queue.get() # blocks if workers too slow + if report is None: # a thread reporting that it finished + unfinished_worker_count -= 1 + logger.info("worker thread finished; awaiting finish of %i more threads", unfinished_worker_count) + continue + examples, trained_words, raw_words = report + job_tally += 1 + + # update progress stats + example_count += examples + trained_word_count += trained_words # only words in vocab & sampled + raw_word_count += raw_words + + # log progress once every report_delay seconds + elapsed = default_timer() - start + if elapsed >= next_report: + self._log_progress( + job_queue, progress_queue, cur_epoch, example_count, total_examples, + raw_word_count, total_words, trained_word_count, elapsed) + next_report = elapsed + report_delay + # all done; report the final stats + elapsed = default_timer() - start + self._log_epoch_end( + cur_epoch, example_count, total_examples, raw_word_count, total_words, + trained_word_count, elapsed, is_corpus_file_mode) + self.total_train_time += elapsed + return trained_word_count, raw_word_count, job_tally + + def _train_epoch_corpusfile( + self, corpus_file, cur_epoch=0, total_examples=None, total_words=None, callbacks=(), **kwargs): + """Train the model for a single epoch. + + Parameters + ---------- + corpus_file : str + Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. + cur_epoch : int, optional + The current training epoch, needed to compute the training parameters for each job. + For example in many implementations the learning rate would be dropping with the number of epochs. + total_examples : int, optional + Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences + in a corpus, used to log progress. + total_words : int + Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw + words in a corpus, used to log progress. Must be provided in order to seek in `corpus_file`. + **kwargs : object + Additional key word parameters for the specific model inheriting from this class. + + Returns + ------- + (int, int, int) + The training report for this epoch consisting of three elements: + * Size of data chunk processed, for example number of sentences in the corpus chunk. + * Effective word count used in training (after ignoring unknown words and trimming the sentence length). + * Total word count used in training. + + """ + if not total_words: + raise ValueError("total_words must be provided alongside corpus_file argument.") + + from gensim.models.word2vec_corpusfile import CythonVocab + from gensim.models.fasttext import FastText + cython_vocab = CythonVocab(self.wv, hs=self.hs, fasttext=isinstance(self, FastText)) + + progress_queue = Queue() + + corpus_file_size = os.path.getsize(corpus_file) + + thread_kwargs = copy.copy(kwargs) + thread_kwargs['cur_epoch'] = cur_epoch + thread_kwargs['total_examples'] = total_examples + thread_kwargs['total_words'] = total_words + workers = [ + threading.Thread( + target=self._worker_loop_corpusfile, + args=( + corpus_file, thread_id, corpus_file_size / self.workers * thread_id, cython_vocab, progress_queue + ), + kwargs=thread_kwargs + ) for thread_id in range(self.workers) + ] + + for thread in workers: + thread.daemon = True + thread.start() + + trained_word_count, raw_word_count, job_tally = self._log_epoch_progress( + progress_queue=progress_queue, job_queue=None, cur_epoch=cur_epoch, + total_examples=total_examples, total_words=total_words, is_corpus_file_mode=True) + + return trained_word_count, raw_word_count, job_tally + + def _train_epoch(self, data_iterable, cur_epoch=0, total_examples=None, total_words=None, + queue_factor=2, report_delay=1.0, callbacks=()): + """Train the model for a single epoch. + + Parameters + ---------- + data_iterable : iterable of list of object + The input corpus. This will be split in chunks and these chunks will be pushed to the queue. + cur_epoch : int, optional + The current training epoch, needed to compute the training parameters for each job. + For example in many implementations the learning rate would be dropping with the number of epochs. + total_examples : int, optional + Count of objects in the `data_iterator`. In the usual case this would correspond to the number of sentences + in a corpus, used to log progress. + total_words : int, optional + Count of total objects in `data_iterator`. In the usual case this would correspond to the number of raw + words in a corpus, used to log progress. + queue_factor : int, optional + Multiplier for size of queue -> size = number of workers * queue_factor. + report_delay : float, optional + Number of seconds between two consecutive progress report messages in the logger. + + Returns + ------- + (int, int, int) + The training report for this epoch consisting of three elements: + * Size of data chunk processed, for example number of sentences in the corpus chunk. + * Effective word count used in training (after ignoring unknown words and trimming the sentence length). + * Total word count used in training. + + """ + job_queue = Queue(maxsize=queue_factor * self.workers) + progress_queue = Queue(maxsize=(queue_factor + 1) * self.workers) + progress_queue.callbacks = callbacks # messy way to pass along for just this session + + workers = [ + threading.Thread( + target=self._worker_loop, + args=(job_queue, progress_queue,)) + for _ in range(self.workers) + ] + + workers.append(threading.Thread( + target=self._job_producer, + args=(data_iterable, job_queue), + kwargs={'cur_epoch': cur_epoch, 'total_examples': total_examples, 'total_words': total_words})) + + for thread in workers: + thread.daemon = True # make interrupting the process with ctrl+c easier + thread.start() + + trained_word_count, raw_word_count, job_tally = self._log_epoch_progress( + progress_queue, job_queue, cur_epoch=cur_epoch, total_examples=total_examples, total_words=total_words, + report_delay=report_delay, is_corpus_file_mode=False) + + return trained_word_count, raw_word_count, job_tally + + def _get_job_params(self, cur_epoch): + """Get the learning rate used in the current epoch. + + Parameters + ---------- + cur_epoch : int + Current iteration through the corpus + + Returns + ------- + float + The learning rate for this epoch (it is linearly reduced with epochs from `self.alpha` to `self.min_alpha`). + + """ + alpha = self.alpha - ((self.alpha - self.min_alpha) * float(cur_epoch) / self.epochs) + return alpha + + def _update_job_params(self, job_params, epoch_progress, cur_epoch): + """Get the correct learning rate for the next iteration. + + Parameters + ---------- + job_params : dict of (str, obj) + UNUSED. + epoch_progress : float + Ratio of finished work in the current epoch. + cur_epoch : int + Number of current iteration. + + Returns + ------- + float + The learning rate to be used in the next training epoch. + + """ + start_alpha = self.alpha + end_alpha = self.min_alpha + progress = (cur_epoch + epoch_progress) / self.epochs + next_alpha = start_alpha - (start_alpha - end_alpha) * progress + next_alpha = max(end_alpha, next_alpha) + self.min_alpha_yet_reached = next_alpha + return next_alpha + + def _get_thread_working_mem(self): + """Computes the memory used per worker thread. + + Returns + ------- + (np.ndarray, np.ndarray) + Each worker threads private work memory. + + """ + work = matutils.zeros_aligned(self.layer1_size, dtype=REAL) # per-thread private work memory + neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL) + return work, neu1 + + def _raw_word_count(self, job): + """Get the number of words in a given job. + + Parameters + ---------- + job: iterable of list of str + The corpus chunk processed in a single batch. + + Returns + ------- + int + Number of raw words in the corpus chunk. + + """ + return sum(len(sentence) for sentence in job) + + def _check_training_sanity(self, epochs=None, total_examples=None, total_words=None, **kwargs): + """Checks whether the training parameters make sense. + + Called right before training starts in :meth:`~gensim.models.base_any2vec.BaseWordEmbeddingsModel.train` + and raises warning or errors depending on the severity of the issue in case an inconsistent parameter + combination is detected. + + Parameters + ---------- + epochs : int, optional + Number of training epochs. Must have a (non None) value. + total_examples : int, optional + Number of documents in the corpus. Either `total_examples` or `total_words` **must** be supplied. + total_words : int, optional + Number of words in the corpus. Either `total_examples` or `total_words` **must** be supplied. + **kwargs : object + Unused. Present to preserve signature among base and inherited implementations. + + Raises + ------ + RuntimeError + If one of the required training pre/post processing steps have not been performed. + ValueError + If the combination of input parameters is inconsistent. + + """ + if self.alpha > self.min_alpha_yet_reached: + logger.warning("Effective 'alpha' higher than previous training cycles") + + if not self.wv.vocab: # should be set by `build_vocab` + raise RuntimeError("you must first build vocabulary before training the model") + if not len(self.wv.vectors): + raise RuntimeError("you must initialize vectors before training the model") + + if not hasattr(self, 'corpus_count'): + raise ValueError( + "The number of examples in the training corpus is missing. " + "Please make sure this is set inside `build_vocab` function." + "Call the `build_vocab` function before calling `train`." + ) + + if total_words is None and total_examples is None: + raise ValueError( + "You must specify either total_examples or total_words, for proper job parameters updation" + "and progress calculations. " + "The usual value is total_examples=model.corpus_count." + ) + if epochs is None: + raise ValueError("You must specify an explict epochs count. The usual value is epochs=model.epochs.") + logger.info( + "training model with %i workers on %i vocabulary and %i features, " + "using sg=%s hs=%s sample=%s negative=%s window=%s", + self.workers, len(self.wv.vocab), self.layer1_size, self.sg, + self.hs, self.sample, self.negative, self.window + ) + + def _log_progress(self, job_queue, progress_queue, cur_epoch, example_count, total_examples, + raw_word_count, total_words, trained_word_count, elapsed): + """Callback used to log progress for long running jobs. + + Parameters + ---------- + job_queue : Queue of (list of object, dict of (str, float)) + The queue of jobs still to be performed by workers. Each job is represented as a tuple containing + the batch of data to be processed and the parameters to be used for the processing as a dict. + progress_queue : Queue of (int, int, int) + A queue of progress reports. Each report is represented as a tuple of these 3 elements: + * size of data chunk processed, for example number of sentences in the corpus chunk. + * Effective word count used in training (after ignoring unknown words and trimming the sentence length). + * Total word count used in training. + cur_epoch : int + The current training iteration through the corpus. + example_count : int + Number of examples (could be sentences for example) processed until now. + total_examples : int + Number of all examples present in the input corpus. + raw_word_count : int + Number of words used in training until now. + total_words : int + Number of all words in the input corpus. + trained_word_count : int + Number of effective words used in training until now (after ignoring unknown words and trimming + the sentence length). + elapsed : int + Elapsed time since the beginning of training in seconds. + + Notes + ----- + If you train the model via `corpus_file` argument, there is no job_queue, so reported job_queue size will + always be equal to -1. + + """ + if total_examples: + # examples-based progress % + logger.info( + "EPOCH %i - PROGRESS: at %.2f%% examples, %.0f words/s, in_qsize %i, out_qsize %i", + cur_epoch + 1, 100.0 * example_count / total_examples, trained_word_count / elapsed, + -1 if job_queue is None else utils.qsize(job_queue), utils.qsize(progress_queue) + ) + else: + # words-based progress % + logger.info( + "EPOCH %i - PROGRESS: at %.2f%% words, %.0f words/s, in_qsize %i, out_qsize %i", + cur_epoch + 1, 100.0 * raw_word_count / total_words, trained_word_count / elapsed, + -1 if job_queue is None else utils.qsize(job_queue), utils.qsize(progress_queue) + ) + + def _log_epoch_end(self, cur_epoch, example_count, total_examples, raw_word_count, total_words, + trained_word_count, elapsed, is_corpus_file_mode): + """Callback used to log the end of a training epoch. + + Parameters + ---------- + cur_epoch : int + The current training iteration through the corpus. + example_count : int + Number of examples (could be sentences for example) processed until now. total_examples : int - Count of sentences. + Number of all examples present in the input corpus. + raw_word_count : int + Number of words used in training until now. total_words : int - Count of raw words in sentences. - epochs : int - Number of iterations (epochs) over the corpus. - start_alpha : float, optional - Initial learning rate. If supplied, replaces the starting `alpha` from the constructor, - for this one call to`train()`. - Use only if making multiple calls to `train()`, when you want to manage the alpha learning-rate yourself - (not recommended). - end_alpha : float, optional - Final learning rate. Drops linearly from `start_alpha`. - If supplied, this replaces the final `min_alpha` from the constructor, for this one call to `train()`. - Use only if making multiple calls to `train()`, when you want to manage the alpha learning-rate yourself - (not recommended). - word_count : int, optional - Count of words already trained. Set this to 0 for the usual - case of training on all words in sentences. - queue_factor : int, optional - Multiplier for size of queue (number of workers * queue_factor). - report_delay : float, optional - Seconds to wait before reporting progress. - compute_loss: bool, optional - If True, computes and stores loss value which can be retrieved using - :meth:`~gensim.models.word2vec.Word2Vec.get_latest_training_loss`. - callbacks : iterable of :class:`~gensim.models.callbacks.CallbackAny2Vec`, optional - Sequence of callbacks to be executed at specific stages during training. + Number of all words in the input corpus. + trained_word_count : int + Number of effective words used in training until now (after ignoring unknown words and trimming + the sentence length). + elapsed : int + Elapsed time since the beginning of training in seconds. + is_corpus_file_mode : bool + Whether training is file-based (corpus_file argument) or not. - Examples + Warnings -------- - .. sourcecode:: pycon + In case the corpus is changed while the epoch was running. - >>> from gensim.models import Word2Vec - >>> sentences = [["cat", "say", "meow"], ["dog", "say", "woof"]] - >>> - >>> model = Word2Vec(min_count=1) - >>> model.build_vocab(sentences) # prepare the model vocabulary - >>> model.train(sentences, total_examples=model.corpus_count, epochs=model.iter) # train word vectors - (1, 30) + """ + logger.info( + "EPOCH - %i : training on %i raw words (%i effective words) took %.1fs, %.0f effective words/s", + cur_epoch + 1, raw_word_count, trained_word_count, elapsed, trained_word_count / elapsed + ) + + # don't warn if training in file-based mode, because it's expected behavior + if is_corpus_file_mode: + return + + # check that the input corpus hasn't changed during iteration + if total_examples and total_examples != example_count: + logger.warning( + "EPOCH - %i : supplied example count (%i) did not equal expected count (%i)", cur_epoch + 1, + example_count, total_examples + ) + if total_words and total_words != raw_word_count: + logger.warning( + "EPOCH - %i : supplied raw word count (%i) did not equal expected count (%i)", cur_epoch + 1, + raw_word_count, total_words + ) + + def _log_train_end(self, raw_word_count, trained_word_count, total_elapsed, job_tally): + """Callback to log the end of training. + + Parameters + ---------- + raw_word_count : int + Number of words used in the whole training. + trained_word_count : int + Number of effective words used in training (after ignoring unknown words and trimming the sentence length). + total_elapsed : int + Total time spent during training in seconds. + job_tally : int + Total number of jobs processed during training. """ - return super(Word2Vec, self).train( - sentences=sentences, corpus_file=corpus_file, total_examples=total_examples, total_words=total_words, - epochs=epochs, start_alpha=start_alpha, end_alpha=end_alpha, word_count=word_count, - queue_factor=queue_factor, report_delay=report_delay, compute_loss=compute_loss, callbacks=callbacks) + logger.info( + "training on a %i raw words (%i effective words) took %.1fs, %.0f effective words/s", + raw_word_count, trained_word_count, total_elapsed, trained_word_count / total_elapsed + ) def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor=2, report_delay=1): """Score the log probability for a sequence of sentences. @@ -547,8 +1648,8 @@ def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor logger.info( "scoring sentences with %i workers on %i vocabulary and %i features, " "using sg=%s hs=%s sample=%s and negative=%s", - self.workers, len(self.wv.vocab), self.trainables.layer1_size, self.sg, self.hs, - self.vocabulary.sample, self.negative + self.workers, len(self.wv.vocab), self.layer1_size, self.sg, self.hs, + self.sample, self.negative ) if not self.wv.vocab: @@ -563,7 +1664,7 @@ def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor def worker_loop(): """Compute log probability for each sentence, lifting lists of sentences from the jobs queue.""" work = zeros(1, dtype=REAL) # for sg hs, we actually only need one memory loc (running sum) - neu1 = matutils.zeros_aligned(self.trainables.layer1_size, dtype=REAL) + neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL) while True: job = job_queue.get() if job is None: # signal to finish @@ -696,7 +1797,7 @@ def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='ut if word in self.wv.vocab: overlap_count += 1 self.wv.vectors[self.wv.vocab[word].index] = weights - self.trainables.vectors_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0=no changes + self.wv.vectors_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0=no changes else: for line_no, line in enumerate(fin): parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") @@ -706,7 +1807,7 @@ def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='ut if word in self.wv.vocab: overlap_count += 1 self.wv.vectors[self.wv.vocab[word].index] = weights - self.trainables.vectors_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0=no changes + self.wv.vectors_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0=no changes logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.vectors.shape, fname) def predict_output_word(self, context_words_list, topn=10): @@ -731,12 +1832,12 @@ def predict_output_word(self, context_words_list, topn=10): "so you need to have run word2vec with negative > 0 for this to work." ) - if not hasattr(self.wv, 'vectors') or not hasattr(self.trainables, 'syn1neg'): + if not hasattr(self.wv, 'vectors') or not hasattr(self, 'syn1neg'): raise RuntimeError("Parameters required for predicting the output words not found.") word_vocabs = [self.wv.vocab[w] for w in context_words_list if w in self.wv.vocab] if not word_vocabs: - warnings.warn("All the input context words are out-of-vocabulary for the current model.") + logger.warning("All the input context words are out-of-vocabulary for the current model.") return None word2_indices = [word.index for word in word_vocabs] @@ -746,7 +1847,7 @@ def predict_output_word(self, context_words_list, topn=10): l1 /= len(word2_indices) # propagate hidden -> output and take softmax to get probabilities - prob_values = exp(dot(l1, self.trainables.syn1neg.T)) + prob_values = exp(dot(l1, self.syn1neg.T)) prob_values /= sum(prob_values) top_indices = matutils.argsort(prob_values, topn=topn, reverse=True) # returning the most probable output words with their probabilities @@ -771,9 +1872,9 @@ def reset_from(self, other_model): """ self.wv.vocab = other_model.wv.vocab self.wv.index2key = other_model.wv.index2key - self.vocabulary.cum_table = other_model.vocabulary.cum_table + self.cum_table = other_model.cum_table self.corpus_count = other_model.corpus_count - self.trainables.reset_weights(self.hs, self.negative, self.wv) + self.reset_weights() def __str__(self): """Human readable representation of the model's state. @@ -816,7 +1917,7 @@ def get_latest_training_loss(self): return self.running_training_loss @classmethod - def load(cls, *args, **kwargs): + def load(cls, *args, rethrow=False, **kwargs): """Load a previously saved :class:`~gensim.models.word2vec.Word2Vec` model. See Also @@ -837,17 +1938,51 @@ def load(cls, *args, **kwargs): """ try: model = super(Word2Vec, cls).load(*args, **kwargs) - - # for backward compatibility for `max_final_vocab` feature + if not isinstance(model, Word2Vec): + rethrow = True + raise AttributeError("Model of type %s can't be loaded by %s" % (type(model), str(cls))) + # for backward compatibility + if not hasattr(model, 'ns_exponent'): + model.ns_exponent = 0.75 + if model.negative and hasattr(model.wv, 'index2word'): + model.make_cum_table() # rebuild cum_table from vocabulary ## TODO: ??? + if not hasattr(model, 'corpus_count'): + model.corpus_count = None + if not hasattr(model, 'corpus_total_words'): + model.corpus_total_words = None + if not hasattr(model.wv, 'vectors_lockf') and hasattr(model.wv, 'vectors'): + model.wv.vectors_lockf = getattr(model, 'vectors_lockf', ones(len(model.wv.vectors), dtype=REAL)) + if not hasattr(model, 'random'): + model.random = np.random.RandomState(model.seed) + if not hasattr(model, 'train_count'): + model.train_count = 0 + model.total_train_time = 0 + if not hasattr(model, 'epochs'): + model.epochs = model.iter + del model.iter if not hasattr(model, 'max_final_vocab'): model.max_final_vocab = None - model.vocabulary.max_final_vocab = None - + if hasattr(model, 'vocabulary'): # re-integrate state that had been moved + for a in ('max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'raw_vocab'): + setattr(model, a, getattr(model.vocabulary, a)) + del model.vocabulary + if hasattr(model, 'trainables'): # re-integrate state that had been moved + for a in ('hashfxn', 'layer1_size', 'seed', 'syn1neg', 'syn1'): + if hasattr(model.trainables, a): + setattr(model, a, getattr(model.trainables, a)) + if hasattr(model, 'syn1'): + model.syn1 = model.syn1 + del model.syn1 + del model.trainables return model - except AttributeError: - logger.info('Model saved using code from earlier Gensim Version. Re-loading old model in a compatible way.') - from gensim.models.deprecated.word2vec import load_old_word2vec - return load_old_word2vec(*args, **kwargs) + except AttributeError as ae: + if rethrow: + raise ae + logger.error( + "Model load error. Was model saved using code from an older Gensim Version? " + "Try loading older model using gensim-3.8.1, then re-saving, to restore " + "compatibility with current code.") + raise ae class BrownCorpus(object): @@ -934,412 +2069,122 @@ 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 line in itertools.islice(self.source, self.limit): - line = utils.to_unicode(line).split() - i = 0 - while i < len(line): - yield line[i: i + self.max_sentence_length] - i += self.max_sentence_length - except AttributeError: - # If it didn't work like a file, use it as a string filename - with utils.open(self.source, 'rb') as fin: - for line in itertools.islice(fin, self.limit): - line = utils.to_unicode(line).split() - i = 0 - while i < len(line): - yield line[i: i + self.max_sentence_length] - i += self.max_sentence_length - - -class PathLineSentences(object): - def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None): - """Like :class:`~gensim.models.word2vec.LineSentence`, but process all files in a directory - in alphabetical order by filename. - - The directory must only contain files that can be read by :class:`gensim.models.word2vec.LineSentence`: - .bz2, .gz, and text files. Any file not ending with .bz2 or .gz is assumed to be a text file. - - The format of files (either text, or compressed text files) in the path is one sentence = one line, - with words already preprocessed and separated by whitespace. - - Warnings - -------- - Does **not recurse** into subdirectories. - - Parameters - ---------- - source : str - Path to the directory. - limit : int or None - Read only the first `limit` lines from each file. Read all if limit is None (the default). - - """ - self.source = source - self.max_sentence_length = max_sentence_length - self.limit = limit - - if os.path.isfile(self.source): - logger.debug('single file given as source, rather than a directory of files') - logger.debug('consider using models.word2vec.LineSentence for a single file') - self.input_files = [self.source] # force code compatibility with list of files - elif os.path.isdir(self.source): - self.source = os.path.join(self.source, '') # ensures os-specific slash at end of path - logger.info('reading directory %s', self.source) - self.input_files = os.listdir(self.source) - self.input_files = [self.source + filename for filename in self.input_files] # make full paths - self.input_files.sort() # makes sure it happens in filename order - else: # not a file or a directory, then we can't do anything with it - raise ValueError('input is neither a file nor a path') - logger.info('files read into PathLineSentences:%s', '\n'.join(self.input_files)) - - def __iter__(self): - """iterate through the files""" - for file_name in self.input_files: - logger.info('reading file %s', file_name) - with utils.open(file_name, 'rb') as fin: - for line in itertools.islice(fin, self.limit): - line = utils.to_unicode(line).split() - i = 0 - while i < len(line): - yield line[i:i + self.max_sentence_length] - i += self.max_sentence_length - - -def _scan_vocab_worker(stream, progress_queue, max_vocab_size=None, trim_rule=None): - """Do an initial scan of all words appearing in stream. - - Note: This function can not be Word2VecVocab's method because - of multiprocessing synchronization specifics in Python. - """ - min_reduce = 1 - vocab = defaultdict(int) - checked_string_types = 0 - sentence_no = -1 - total_words = 0 - for sentence_no, sentence in enumerate(stream): - if not checked_string_types: - if isinstance(sentence, string_types): - log_msg = "Each 'sentences' item should be a list of words (usually unicode strings). " \ - "First item here is instead plain %s." % type(sentence) - progress_queue.put(log_msg) - - checked_string_types += 1 - - for word in sentence: - vocab[word] += 1 - - if max_vocab_size and len(vocab) > max_vocab_size: - utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule) - min_reduce += 1 - - total_words += len(sentence) - - progress_queue.put((total_words, sentence_no + 1)) - progress_queue.put(None) - return vocab - - -@dataclass -class W2VVocab: - """A dataclass shape-compatible with keyedvectors.SimpleVocab, extended with the - `sample_int` property needed by `Word2Vec` models.""" - __slots__ = ('count', 'index', 'sample_int') - count: int - index: int - sample_int: int - - def __init__(self, count=0, index=0, sample_int=2**32): - self.count, self.index, self.sample_int = count, index, sample_int - - def __lt__(self, other): - return self.count < other.count - - -@dataclass -class W2VHSVocab: - """A dataclass shape-compatible with W2VVocab, extended with the `code` and - `point` properties needed by hierarchical-sampling (`hs=1`) `Word2Vec` models.""" - __slots__ = ('count', 'index', 'sample_int', 'code', 'point') - count: int - index: int - sample_int: int - code: List[int] - point: List[int] - - def __init__(self, count=0, index=0, sample_int=2**32, code=None, point=None): - self.count, self.index, self.sample_int, self.code, self.point = \ - count, index, sample_int, code, point - - def __lt__(self, other): - return self.count < other.count - - -class Word2VecVocab(utils.SaveLoad): - def __init__( - self, max_vocab_size=None, min_count=5, sample=1e-3, sorted_vocab=True, null_word=0, - max_final_vocab=None, ns_exponent=0.75): - """Vocabulary used by :class:`~gensim.models.word2vec.Word2Vec`.""" - self.max_vocab_size = max_vocab_size - self.min_count = min_count - self.sample = sample - self.sorted_vocab = sorted_vocab - self.null_word = null_word - self.cum_table = None # for negative sampling - self.raw_vocab = None - self.max_final_vocab = max_final_vocab - self.ns_exponent = ns_exponent - - def _scan_vocab(self, sentences, progress_per, trim_rule): - sentence_no = -1 - total_words = 0 - min_reduce = 1 - vocab = defaultdict(int) - checked_string_types = 0 - for sentence_no, sentence in enumerate(sentences): - if not checked_string_types: - if isinstance(sentence, string_types): - logger.warning( - "Each 'sentences' item should be a list of words (usually unicode strings). " - "First item here is instead plain %s.", - type(sentence) - ) - checked_string_types += 1 - if sentence_no % progress_per == 0: - logger.info( - "PROGRESS: at sentence #%i, processed %i words, keeping %i word types", - sentence_no, total_words, len(vocab) - ) - for word in sentence: - vocab[word] += 1 - total_words += len(sentence) - - 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 - - corpus_count = sentence_no + 1 - self.raw_vocab = vocab - return total_words, corpus_count - - def scan_vocab(self, sentences=None, corpus_file=None, progress_per=10000, workers=None, trim_rule=None): - logger.info("collecting all words and their counts") - if corpus_file: - sentences = LineSentence(corpus_file) - - total_words, corpus_count = self._scan_vocab(sentences, progress_per, trim_rule) - - logger.info( - "collected %i word types from a corpus of %i raw words and %i sentences", - len(self.raw_vocab), total_words, corpus_count - ) - - return total_words, corpus_count - - def sort_vocab(self, wv): - """Sort the vocabulary so the most frequent words have the lowest indexes.""" - if len(wv.vectors): - raise RuntimeError("cannot sort vocabulary after model weights already initialized.") - wv.index2key.sort(key=lambda word: wv.vocab[word].count, reverse=True) - for i, word in enumerate(wv.index2key): - wv.vocab[word].index = i - - def prepare_vocab( - self, hs, negative, wv, update=False, keep_raw_vocab=False, trim_rule=None, - min_count=None, sample=None, dry_run=False): - """Apply vocabulary settings for `min_count` (discarding less-frequent words) - and `sample` (controlling the downsampling of more-frequent words). - - Calling with `dry_run=True` will only simulate the provided settings and - report the size of the retained vocabulary, effective corpus length, and - estimated memory requirements. Results are both printed via logging and - returned as a dict. - - Delete the raw vocabulary after the scaling is done to free up RAM, - unless `keep_raw_vocab` is set. - - """ - min_count = min_count or self.min_count - sample = sample or self.sample - drop_total = drop_unique = 0 + # Things that don't have seek will trigger an exception + self.source.seek(0) + for line in itertools.islice(self.source, self.limit): + line = utils.to_unicode(line).split() + i = 0 + while i < len(line): + yield line[i: i + self.max_sentence_length] + i += self.max_sentence_length + except AttributeError: + # If it didn't work like a file, use it as a string filename + with utils.open(self.source, 'rb') as fin: + for line in itertools.islice(fin, self.limit): + line = utils.to_unicode(line).split() + i = 0 + while i < len(line): + yield line[i: i + self.max_sentence_length] + i += self.max_sentence_length - # set effective_min_count to min_count in case max_final_vocab isn't set - self.effective_min_count = min_count - # if max_final_vocab is specified instead of min_count - # pick a min_count which satisfies max_final_vocab as well as possible - if self.max_final_vocab is not None: - sorted_vocab = sorted(self.raw_vocab.keys(), key=lambda word: self.raw_vocab[word], reverse=True) - calc_min_count = 1 +class PathLineSentences(object): + def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None): + """Like :class:`~gensim.models.word2vec.LineSentence`, but process all files in a directory + in alphabetical order by filename. - if self.max_final_vocab < len(sorted_vocab): - calc_min_count = self.raw_vocab[sorted_vocab[self.max_final_vocab]] + 1 + The directory must only contain files that can be read by :class:`gensim.models.word2vec.LineSentence`: + .bz2, .gz, and text files. Any file not ending with .bz2 or .gz is assumed to be a text file. - self.effective_min_count = max(calc_min_count, min_count) - logger.info( - "max_final_vocab=%d and min_count=%d resulted in calc_min_count=%d, effective_min_count=%d", - self.max_final_vocab, min_count, calc_min_count, self.effective_min_count - ) + The format of files (either text, or compressed text files) in the path is one sentence = one line, + with words already preprocessed and separated by whitespace. - if not update: - logger.info("Loading a fresh vocabulary") - retain_total, retain_words = 0, [] - # Discard words less-frequent than min_count - if not dry_run: - wv.index2key = [] - # make stored settings match these applied settings - self.min_count = min_count - self.sample = sample - wv.vocab = {} + Warnings + -------- + Does **not recurse** into subdirectories. - for word, v in iteritems(self.raw_vocab): - if keep_vocab_item(word, v, self.effective_min_count, trim_rule=trim_rule): - retain_words.append(word) - retain_total += v - if not dry_run: - wv.vocab[word] = W2VVocab(count=v, index=len(wv.index2key)) - wv.index2key.append(word) - else: - drop_unique += 1 - drop_total += v - original_unique_total = len(retain_words) + drop_unique - retain_unique_pct = len(retain_words) * 100 / max(original_unique_total, 1) - logger.info( - "effective_min_count=%d retains %i unique words (%i%% of original %i, drops %i)", - self.effective_min_count, len(retain_words), retain_unique_pct, original_unique_total, drop_unique - ) - original_total = retain_total + drop_total - retain_pct = retain_total * 100 / max(original_total, 1) - logger.info( - "effective_min_count=%d leaves %i word corpus (%i%% of original %i, drops %i)", - self.effective_min_count, retain_total, retain_pct, original_total, drop_total - ) - else: - logger.info("Updating model with new vocabulary") - new_total = pre_exist_total = 0 - new_words = pre_exist_words = [] - for word, v in iteritems(self.raw_vocab): - if keep_vocab_item(word, v, self.effective_min_count, trim_rule=trim_rule): - if word in wv.vocab: - pre_exist_words.append(word) - pre_exist_total += v - if not dry_run: - wv.vocab[word].count += v - else: - new_words.append(word) - new_total += v - if not dry_run: - wv.vocab[word] = W2VVocab(count=v, index=len(wv.index2key)) - wv.index2key.append(word) - else: - drop_unique += 1 - drop_total += v - original_unique_total = len(pre_exist_words) + len(new_words) + drop_unique - pre_exist_unique_pct = len(pre_exist_words) * 100 / max(original_unique_total, 1) - new_unique_pct = len(new_words) * 100 / max(original_unique_total, 1) - logger.info( - "New added %i unique words (%i%% of original %i) " - "and increased the count of %i pre-existing words (%i%% of original %i)", - len(new_words), new_unique_pct, original_unique_total, len(pre_exist_words), - pre_exist_unique_pct, original_unique_total - ) - retain_words = new_words + pre_exist_words - retain_total = new_total + pre_exist_total + Parameters + ---------- + source : str + Path to the directory. + limit : int or None + Read only the first `limit` lines from each file. Read all if limit is None (the default). - # Precalculate each vocabulary item's threshold for sampling - if not sample: - # no words downsampled - threshold_count = retain_total - elif sample < 1.0: - # traditional meaning: set parameter as proportion of total - threshold_count = sample * retain_total - else: - # new shorthand: sample >= 1 means downsample all words with higher count than sample - threshold_count = int(sample * (3 + sqrt(5)) / 2) + """ + self.source = source + self.max_sentence_length = max_sentence_length + self.limit = limit - downsample_total, downsample_unique = 0, 0 - for w in retain_words: - v = self.raw_vocab[w] - word_probability = (sqrt(v / threshold_count) + 1) * (threshold_count / v) - if word_probability < 1.0: - downsample_unique += 1 - downsample_total += word_probability * v - else: - word_probability = 1.0 - downsample_total += v - if not dry_run: - wv.vocab[w].sample_int = int(round(word_probability * 2**32)) + if os.path.isfile(self.source): + logger.debug('single file given as source, rather than a directory of files') + logger.debug('consider using models.word2vec.LineSentence for a single file') + self.input_files = [self.source] # force code compatibility with list of files + elif os.path.isdir(self.source): + self.source = os.path.join(self.source, '') # ensures os-specific slash at end of path + logger.info('reading directory %s', self.source) + self.input_files = os.listdir(self.source) + self.input_files = [self.source + filename for filename in self.input_files] # make full paths + self.input_files.sort() # makes sure it happens in filename order + else: # not a file or a directory, then we can't do anything with it + raise ValueError('input is neither a file nor a path') + logger.info('files read into PathLineSentences:%s', '\n'.join(self.input_files)) - if not dry_run and not keep_raw_vocab: - logger.info("deleting the raw counts dictionary of %i items", len(self.raw_vocab)) - self.raw_vocab = defaultdict(int) + def __iter__(self): + """iterate through the files""" + for file_name in self.input_files: + logger.info('reading file %s', file_name) + with utils.open(file_name, 'rb') as fin: + for line in itertools.islice(fin, self.limit): + line = utils.to_unicode(line).split() + i = 0 + while i < len(line): + yield line[i:i + self.max_sentence_length] + i += self.max_sentence_length - logger.info("sample=%g downsamples %i most-common words", sample, downsample_unique) - logger.info( - "downsampling leaves estimated %i word corpus (%.1f%% of prior %i)", - downsample_total, downsample_total * 100.0 / max(retain_total, 1), retain_total - ) - # return from each step: words-affected, resulting-corpus-size, extra memory estimates - report_values = { - 'drop_unique': drop_unique, 'retain_total': retain_total, 'downsample_unique': downsample_unique, - 'downsample_total': int(downsample_total), 'num_retained_words': len(retain_words) - } +@dataclass +class W2VVocab: + """A dataclass shape-compatible with keyedvectors.SimpleVocab, extended with the + `sample_int` property needed by `Word2Vec` models.""" + __slots__ = ('count', 'index', 'sample_int') + count: int + index: int + sample_int: int - if self.null_word: - # create null pseudo-word for padding when using concatenative L1 (run-of-words) - # this word is only ever input – never predicted – so count, huffman-point, etc doesn't matter - self.add_null_word(wv) + def __init__(self, count=0, index=0, sample_int=2**32): + self.count, self.index, self.sample_int = count, index, sample_int - if self.sorted_vocab and not update: - self.sort_vocab(wv) - if hs: - # add info about each word's Huffman encoding - self.create_binary_tree(wv) - if negative: - # build the table for drawing random words (for negative sampling) - self.make_cum_table(wv) + def __lt__(self, other): + return self.count < other.count - return report_values - def add_null_word(self, wv): - word, v = '\0', W2VVocab(count=1, sample_int=0) - v.index = len(wv.vocab) - wv.index2key.append(word) - wv.vocab[word] = v +@dataclass +class W2VHSVocab: + """A dataclass shape-compatible with W2VVocab, extended with the `code` and + `point` properties needed by hierarchical-sampling (`hs=1`) `Word2Vec` models.""" + __slots__ = ('count', 'index', 'sample_int', 'code', 'point') + count: int + index: int + sample_int: int + code: List[int] + point: List[int] - def create_binary_tree(self, wv): - """Create a `binary Huffman tree `_ using stored vocabulary - word counts. Frequent words will have shorter binary codes. - Called internally from :meth:`~gensim.models.word2vec.Word2VecVocab.build_vocab`. + def __init__(self, count=0, index=0, sample_int=2**32, code=None, point=None): + self.count, self.index, self.sample_int, self.code, self.point = \ + count, index, sample_int, code, point - """ - _assign_binary_codes(wv.vocab) + def __lt__(self, other): + return self.count < other.count - def make_cum_table(self, wv, domain=2**31 - 1): - """Create a cumulative-distribution table using stored vocabulary word counts for - drawing random words in the negative-sampling training routines. - To draw a word index, choose a random integer up to the maximum value in the table (cum_table[-1]), - then finding that integer's sorted insertion point (as if by `bisect_left` or `ndarray.searchsorted()`). - That insertion point is the drawn index, coming up in proportion equal to the increment at that slot. +class Word2VecVocab(utils.SaveLoad): + """Obsolete class retained for now as load-compatibility state capture""" + pass - Called internally from :meth:`~gensim.models.word2vec.Word2VecVocab.build_vocab`. - """ - vocab_size = len(wv.index2key) - self.cum_table = zeros(vocab_size, dtype=uint32) - # compute sum of all power (Z in paper) - train_words_pow = 0.0 - for word_index in range(vocab_size): - train_words_pow += wv.vocab[wv.index2key[word_index]].count**self.ns_exponent - cumulative = 0.0 - for word_index in range(vocab_size): - cumulative += wv.vocab[wv.index2key[word_index]].count**self.ns_exponent - self.cum_table[word_index] = round(cumulative / train_words_pow * domain) - if len(self.cum_table) > 0: - assert self.cum_table[-1] == domain +class Word2VecTrainables(utils.SaveLoad): + """Obsolete class retained for now as load-compatibility state capture""" + pass class Heapitem(namedtuple('Heapitem', 'count, index, left, right')): @@ -1409,62 +2254,6 @@ def _assign_binary_codes(vocab): logger.info("built huffman tree with maximum node depth %i", max_depth) -class Word2VecTrainables(utils.SaveLoad): - def __init__(self, vector_size=100, seed=1, hashfxn=hash): - """Represents the inner shallow neural network used to train :class:`~gensim.models.word2vec.Word2Vec`.""" - self.hashfxn = hashfxn - self.layer1_size = vector_size - self.seed = seed - - def prepare_weights(self, hs, negative, wv, update=False, vocabulary=None): - """Build tables and model weights based on final vocabulary settings.""" - # set initial input/projection and hidden weights - if not update: - self.reset_weights(hs, negative, wv) - else: - self.update_weights(hs, negative, wv) - - @deprecated("Use gensim.models.keyedvectors.pseudorandom_weak_vector() directly") - def seeded_vector(self, seed_string, vector_size): - return pseudorandom_weak_vector(vector_size, seed_string=seed_string, hashfxn=self.hashfxn) - - def reset_weights(self, hs, negative, wv): - """Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary.""" - logger.info("resetting layer weights") - wv.resize_vectors() - wv.randomly_initialize_vectors(seed=self.seed) - if hs: - self.syn1 = zeros((len(wv.vocab), self.layer1_size), dtype=REAL) - if negative: - self.syn1neg = zeros((len(wv.vocab), self.layer1_size), dtype=REAL) - - self.vectors_lockf = ones(len(wv.vocab), dtype=REAL) # zeros suppress learning - - def update_weights(self, hs, negative, wv): - """Copy all the existing weights, and reset the weights for the newly added vocabulary.""" - logger.info("updating layer weights") - new_range = wv.resize_vectors() - gained_vocab = len(new_range) - wv.randomly_initialize_vectors(indexes=new_range) - - # Raise an error if an online update is run before initial training on a corpus - if not len(wv.vectors): - raise RuntimeError( - "You cannot do an online vocabulary-update of a model which has no prior vocabulary. " - "First build the vocabulary of your model with a corpus before doing an online update." - ) - - if hs: - self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=REAL)]) - if negative: - pad = zeros((gained_vocab, self.layer1_size), dtype=REAL) - self.syn1neg = vstack([self.syn1neg, pad]) - wv.vectors_norm = None - - # do not suppress learning for already learned words - self.vectors_lockf = ones(len(wv.vocab), dtype=REAL) # zeros suppress learning - - # Example: ./word2vec.py -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 \ # -negative 5 -hs 0 -binary 0 -cbow 1 -iter 3 if __name__ == "__main__": diff --git a/gensim/models/word2vec_inner.pyx b/gensim/models/word2vec_inner.pyx index 0576773bd5..076ff54b1c 100755 --- a/gensim/models/word2vec_inner.pyx +++ b/gensim/models/word2vec_inner.pyx @@ -467,7 +467,7 @@ cdef unsigned long long w2v_fast_sentence_cbow_neg( cdef init_w2v_config(Word2VecConfig *c, model, alpha, compute_loss, _work, _neu1=None): c[0].hs = model.hs c[0].negative = model.negative - c[0].sample = (model.vocabulary.sample != 0) + c[0].sample = (model.sample != 0) c[0].cbow_mean = model.cbow_mean c[0].window = model.window c[0].workers = model.workers @@ -476,17 +476,17 @@ cdef init_w2v_config(Word2VecConfig *c, model, alpha, compute_loss, _work, _neu1 c[0].running_training_loss = model.running_training_loss c[0].syn0 = (np.PyArray_DATA(model.wv.vectors)) - c[0].word_locks = (np.PyArray_DATA(model.trainables.vectors_lockf)) + c[0].word_locks = (np.PyArray_DATA(model.wv.vectors_lockf)) c[0].alpha = alpha c[0].size = model.wv.vector_size if c[0].hs: - c[0].syn1 = (np.PyArray_DATA(model.trainables.syn1)) + c[0].syn1 = (np.PyArray_DATA(model.syn1)) if c[0].negative: - c[0].syn1neg = (np.PyArray_DATA(model.trainables.syn1neg)) - c[0].cum_table = (np.PyArray_DATA(model.vocabulary.cum_table)) - c[0].cum_table_len = len(model.vocabulary.cum_table) + c[0].syn1neg = (np.PyArray_DATA(model.syn1neg)) + c[0].cum_table = (np.PyArray_DATA(model.cum_table)) + c[0].cum_table_len = len(model.cum_table) if c[0].negative or c[0].sample: c[0].next_random = (2**24) * model.random.randint(0, 2**24) + model.random.randint(0, 2**24) @@ -709,7 +709,7 @@ def score_sentence_sg(model, sentence, _work): cdef long result = 0 cdef int sentence_len - c.syn1 = (np.PyArray_DATA(model.trainables.syn1)) + c.syn1 = (np.PyArray_DATA(model.syn1)) # convert Python structures to primitive types, so we can release the GIL c.work = np.PyArray_DATA(_work) @@ -804,7 +804,7 @@ def score_sentence_cbow(model, sentence, _work, _neu1): cdef int i, j, k cdef long result = 0 - c.syn1 = (np.PyArray_DATA(model.trainables.syn1)) + c.syn1 = (np.PyArray_DATA(model.syn1)) # convert Python structures to primitive types, so we can release the GIL c.work = np.PyArray_DATA(_work) diff --git a/gensim/models/wrappers/__init__.py b/gensim/models/wrappers/__init__.py index 9cd14ea8e7..330abce500 100644 --- a/gensim/models/wrappers/__init__.py +++ b/gensim/models/wrappers/__init__.py @@ -5,6 +5,5 @@ from .ldamallet import LdaMallet # noqa:F401 from .dtmmodel import DtmModel # noqa:F401 from .ldavowpalwabbit import LdaVowpalWabbit # noqa:F401 -from .fasttext import FastText # noqa:F401 from .wordrank import Wordrank # noqa:F401 from .varembed import VarEmbed # noqa:F401 diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py deleted file mode 100644 index bca36c7cb9..0000000000 --- a/gensim/models/wrappers/fasttext.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Author: Jayant Jain -# Copyright (C) 2017 Radim Rehurek -# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html - - -""" -Warnings --------- -.. deprecated:: 3.2.0 - Use :mod:`gensim.models.fasttext` instead. - - - -Python wrapper around word representation learning from FastText, a library for efficient learning -of word representations and sentence classification [1]. - -This module allows training a word embedding from a training corpus with the additional ability -to obtain word vectors for out-of-vocabulary words, using the fastText C implementation. - -The wrapped model can NOT be updated with new documents for online training -- use gensim's -`Word2Vec` for that. - -Example: - -.. sourcecode:: pycon - - >>> from gensim.models.wrappers import FastText - >>> model = FastText.train('/Users/kofola/fastText/fasttext', corpus_file='text8') - >>> print(model['forests']) # prints vector for given out-of-vocabulary word - -.. [1] https://github.com/facebookresearch/fastText#enriching-word-vectors-with-subword-information - - - -""" -from gensim.models.deprecated.fasttext_wrapper import FastText, FastTextKeyedVectors # noqa:F401 -from gensim.models.deprecated.fasttext_wrapper import ft_hash, compute_ngrams # noqa:F401 diff --git a/gensim/sklearn_api/d2vmodel.py b/gensim/sklearn_api/d2vmodel.py index fa154a2497..c49d1b2baf 100644 --- a/gensim/sklearn_api/d2vmodel.py +++ b/gensim/sklearn_api/d2vmodel.py @@ -36,9 +36,10 @@ class D2VTransformer(TransformerMixin, BaseEstimator): """ def __init__(self, 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, size=100, alpha=0.025, window=5, min_count=5, - max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, hs=0, negative=5, cbow_mean=1, - hashfxn=hash, iter=5, sorted_vocab=1, batch_words=10000): + docvecs_mapfile=None, comment=None, trim_rule=None, vector_size=100, alpha=0.025, window=5, + min_count=5, max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, + hs=0, negative=5, cbow_mean=1, + hashfxn=hash, epochs=5, sorted_vocab=1, batch_words=10000): """ Parameters @@ -72,7 +73,7 @@ def __init__(self, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1 be trimmed away (:attr:`gensim.utils.RULE_DISCARD`), or handled using the default (:attr:`gensim.utils.RULE_DEFAULT`). If None, then :func:`gensim.utils.keep_vocab_item` will be used. - size : int, optional + vector_size : int, optional Dimensionality of the feature vectors. alpha : float, optional The initial learning rate. @@ -108,7 +109,7 @@ def __init__(self, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1 Same as `dm_mean`, **unused**. hashfxn : function (object -> int), optional A hashing function. Used to create an initial random reproducible vector by hashing the random seed. - iter : int, optional + epochs : int, optional Number of epochs to iterate through the corpus. sorted_vocab : bool, optional Whether the vocabulary should be sorted internally. @@ -128,7 +129,7 @@ def __init__(self, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1 self.trim_rule = trim_rule # attributes associated with gensim.models.Word2Vec - self.size = size + self.vector_size = vector_size self.alpha = alpha self.window = window self.min_count = min_count @@ -141,7 +142,7 @@ def __init__(self, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1 self.negative = negative self.cbow_mean = int(cbow_mean) self.hashfxn = hashfxn - self.iter = iter + self.epochs = epochs self.sorted_vocab = sorted_vocab self.batch_words = batch_words @@ -167,11 +168,11 @@ def fit(self, X, y=None): documents=d2v_sentences, dm_mean=self.dm_mean, dm=self.dm, dbow_words=self.dbow_words, dm_concat=self.dm_concat, dm_tag_count=self.dm_tag_count, docvecs=self.docvecs, docvecs_mapfile=self.docvecs_mapfile, comment=self.comment, - trim_rule=self.trim_rule, vector_size=self.size, alpha=self.alpha, window=self.window, + trim_rule=self.trim_rule, vector_size=self.vector_size, alpha=self.alpha, window=self.window, min_count=self.min_count, max_vocab_size=self.max_vocab_size, sample=self.sample, seed=self.seed, workers=self.workers, min_alpha=self.min_alpha, hs=self.hs, negative=self.negative, cbow_mean=self.cbow_mean, hashfxn=self.hashfxn, - epochs=self.iter, sorted_vocab=self.sorted_vocab, batch_words=self.batch_words + epochs=self.epochs, sorted_vocab=self.sorted_vocab, batch_words=self.batch_words ) return self diff --git a/gensim/sklearn_api/ftmodel.py b/gensim/sklearn_api/ftmodel.py index a1edd6c338..7acd22cfc2 100644 --- a/gensim/sklearn_api/ftmodel.py +++ b/gensim/sklearn_api/ftmodel.py @@ -18,7 +18,7 @@ >>> from gensim.sklearn_api import FTTransformer >>> >>> # Create a model to represent each word by a 10 dimensional vector. - >>> model = FTTransformer(size=10, min_count=1, seed=1) + >>> model = FTTransformer(vector_size=10, min_count=1, seed=1) >>> >>> # What is the vector representations of the word 'graph' and 'system'? >>> wordvecs = model.fit(common_texts).transform(['graph', 'system']) @@ -56,10 +56,10 @@ class FTTransformer(TransformerMixin, BaseEstimator): Information `_. """ - def __init__(self, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, + def __init__(self, sg=0, hs=0, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, word_ngrams=1, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, negative=5, ns_exponent=0.75, - cbow_mean=1, hashfxn=hash, iter=5, null_word=0, min_n=3, + cbow_mean=1, hashfxn=hash, epochs=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, bucket=2000000, trim_rule=None, batch_words=10000): """ @@ -71,7 +71,7 @@ def __init__(self, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, hs : {1,0}, optional If 1, hierarchical softmax will be used for model training. If set to 0, and `negative` is non-zero, negative sampling will be used. - size : int, optional + vector_size : int, optional Dimensionality of the word vectors. alpha : float, optional The initial learning rate. @@ -113,7 +113,7 @@ def __init__(self, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used. hashfxn : function, optional Hash function to use to randomly initialize weights, for increased training reproducibility. - iter : int, optional + epochs : int, optional Number of iterations (epochs) over the corpus. min_n : int, optional Minimum length of char n-grams to be used for training word representations. @@ -148,7 +148,7 @@ def __init__(self, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, self.gensim_model = None self.sg = sg self.hs = hs - self.size = size + self.vector_size = vector_size self.alpha = alpha self.window = window self.min_count = min_count @@ -162,7 +162,7 @@ def __init__(self, sg=0, hs=0, size=100, alpha=0.025, window=5, min_count=5, self.ns_exponent = ns_exponent self.cbow_mean = cbow_mean self.hashfxn = hashfxn - self.iter = iter + self.epochs = epochs self.null_word = null_word self.min_n = min_n self.max_n = max_n @@ -189,13 +189,13 @@ def fit(self, X, y=None): """ self.gensim_model = models.FastText( - sentences=X, sg=self.sg, hs=self.hs, size=self.size, + sentences=X, sg=self.sg, hs=self.hs, vector_size=self.vector_size, alpha=self.alpha, window=self.window, min_count=self.min_count, max_vocab_size=self.max_vocab_size, word_ngrams=self.word_ngrams, sample=self.sample, seed=self.seed, workers=self.workers, min_alpha=self.min_alpha, negative=self.negative, ns_exponent=self.ns_exponent, cbow_mean=self.cbow_mean, - hashfxn=self.hashfxn, iter=self.iter, null_word=self.null_word, + hashfxn=self.hashfxn, epochs=self.epochs, null_word=self.null_word, min_n=self.min_n, max_n=self.max_n, sorted_vocab=self.sorted_vocab, bucket=self.bucket, trim_rule=self.trim_rule, batch_words=self.batch_words @@ -212,7 +212,7 @@ def transform(self, words): Returns ------- - np.ndarray of shape [`len(words)`, `size`] + np.ndarray of shape [`len(words)`, `vector_size`] A 2D array where each row is the vector of one word. """ @@ -225,4 +225,4 @@ def transform(self, words): if isinstance(words, six.string_types): words = [words] vectors = [self.gensim_model.wv[word] for word in words] - return np.reshape(np.array(vectors), (len(words), self.size)) + return np.reshape(np.array(vectors), (len(words), self.vector_size)) diff --git a/gensim/sklearn_api/w2vmodel.py b/gensim/sklearn_api/w2vmodel.py index 07091c2dde..ae64b56e3e 100644 --- a/gensim/sklearn_api/w2vmodel.py +++ b/gensim/sklearn_api/w2vmodel.py @@ -18,7 +18,7 @@ >>> from gensim.sklearn_api import W2VTransformer >>> >>> # Create a model to represent each word by a 10 dimensional vector. - >>> model = W2VTransformer(size=10, min_count=1, seed=1) + >>> model = W2VTransformer(vector_size=10, min_count=1, seed=1) >>> >>> # What is the vector representation of the word 'graph'? >>> wordvecs = model.fit(common_texts).transform(['graph', 'system']) @@ -40,14 +40,14 @@ class W2VTransformer(TransformerMixin, BaseEstimator): Estimation of Word Representations in Vector Space" `_. """ - def __init__(self, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1, - workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0, + def __init__(self, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1, + workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5, cbow_mean=1, hashfxn=hash, epochs=5, null_word=0, trim_rule=None, sorted_vocab=1, batch_words=10000): """ Parameters ---------- - size : int + vector_size : int Dimensionality of the feature vectors. alpha : float The initial learning rate. @@ -85,7 +85,7 @@ def __init__(self, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size= If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used. hashfxn : callable (object -> int), optional A hashing function. Used to create an initial random reproducible vector by hashing the random seed. - iter : int + epochs : int Number of iterations (epochs) over the corpus. null_word : int {1, 0} If 1, a null pseudo-word will be created for padding when using concatenative L1 (run-of-words) @@ -106,7 +106,7 @@ def __init__(self, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size= """ self.gensim_model = None - self.size = size + self.vector_size = vector_size self.alpha = alpha self.window = window self.min_count = min_count @@ -120,7 +120,7 @@ def __init__(self, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size= self.negative = negative self.cbow_mean = int(cbow_mean) self.hashfxn = hashfxn - self.iter = iter + self.epochs = epochs self.null_word = null_word self.trim_rule = trim_rule self.sorted_vocab = sorted_vocab @@ -144,11 +144,11 @@ def fit(self, X, y=None): """ self.gensim_model = models.Word2Vec( - sentences=X, size=self.size, alpha=self.alpha, + sentences=X, vector_size=self.vector_size, alpha=self.alpha, window=self.window, min_count=self.min_count, max_vocab_size=self.max_vocab_size, sample=self.sample, seed=self.seed, workers=self.workers, min_alpha=self.min_alpha, sg=self.sg, hs=self.hs, negative=self.negative, cbow_mean=self.cbow_mean, - hashfxn=self.hashfxn, iter=self.iter, null_word=self.null_word, trim_rule=self.trim_rule, + hashfxn=self.hashfxn, epochs=self.epochs, null_word=self.null_word, trim_rule=self.trim_rule, sorted_vocab=self.sorted_vocab, batch_words=self.batch_words ) return self @@ -163,7 +163,7 @@ def transform(self, words): Returns ------- - np.ndarray of shape [`len(words)`, `size`] + np.ndarray of shape [`len(words)`, `vector_size`] A 2D array where each row is the vector of one word. """ @@ -176,7 +176,7 @@ def transform(self, words): if isinstance(words, six.string_types): words = [words] vectors = [self.gensim_model.wv[word] for word in words] - return np.reshape(np.array(vectors), (len(words), self.size)) + return np.reshape(np.array(vectors), (len(words), self.vector_size)) def partial_fit(self, X): raise NotImplementedError( diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index 5bad1d8539..40b5ab2ece 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -75,7 +75,7 @@ def test_persistence(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_persistence_fromfile(self): """Test storing/loading the entire model.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) tmpf = get_tmpfile('gensim_doc2vec.tst') @@ -102,7 +102,7 @@ def testPersistenceWord2VecFormat(self): binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_word, binary=True) self.assertEqual(len(model.wv.vocab), len(binary_model_dv.vocab)) - def testLoadOldModel(self): + def obsolete_testLoadOldModel(self): """Test loading an old doc2vec model from indeterminate version""" model_file = 'doc2vec_old' # which version?!? @@ -111,17 +111,17 @@ def testLoadOldModel(self): self.assertTrue(len(model.wv.vocab) == 3955) self.assertTrue(len(model.wv.index2word) == 3955) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.trainables.vectors_lockf.shape == (3955, )) - self.assertTrue(model.vocabulary.cum_table.shape == (3955, )) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.wv.vectors_lockf.shape == (3955, )) + self.assertTrue(model.cum_table.shape == (3955, )) self.assertTrue(model.docvecs.vectors.shape == (300, 100)) - self.assertTrue(model.trainables.vectors_docs_lockf.shape == (300, )) + self.assertTrue(model.docvecs.vectors_lockf.shape == (300, )) self.assertTrue(len(model.docvecs) == 300) self.model_sanity(model) - def testLoadOldModelSeparates(self): + def obsolete_testLoadOldModelSeparates(self): """Test loading an old doc2vec model from indeterminate version""" # Model stored in multiple files @@ -131,16 +131,16 @@ def testLoadOldModelSeparates(self): self.assertTrue(len(model.wv.vocab) == 3955) self.assertTrue(len(model.wv.index2word) == 3955) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.trainables.vectors_lockf.shape == (3955, )) - self.assertTrue(model.vocabulary.cum_table.shape == (3955, )) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.wv.vectors_lockf.shape == (3955, )) + self.assertTrue(model.cum_table.shape == (3955, )) self.assertTrue(model.docvecs.vectors.shape == (300, 100)) - self.assertTrue(model.trainables.vectors_docs_lockf.shape == (300, )) + self.assertTrue(model.docvecs.vectors_lockf.shape == (300, )) self.assertTrue(len(model.docvecs) == 300) self.model_sanity(model) - def test_load_old_models_pre_1_0(self): + def obsolete_test_load_old_models_pre_1_0(self): """Test loading pre-1.0 models""" model_file = 'd2v-lee-v0.13.0' model = doc2vec.Doc2Vec.load(datapath(model_file)) @@ -153,7 +153,7 @@ def test_load_old_models_pre_1_0(self): for old_version in old_versions: self._check_old_version(old_version) - def test_load_old_models_1_x(self): + def obsolete_test_load_old_models_1_x(self): """Test loading 1.x models""" old_versions = [ '1.0.0', '1.0.1', @@ -161,7 +161,7 @@ def test_load_old_models_1_x(self): for old_version in old_versions: self._check_old_version(old_version) - def test_load_old_models_2_x(self): + def obsolete_test_load_old_models_2_x(self): """Test loading 2.x models""" old_versions = [ '2.0.0', '2.1.0', '2.2.0', '2.3.0', @@ -169,10 +169,18 @@ def test_load_old_models_2_x(self): for old_version in old_versions: self._check_old_version(old_version) - def test_load_old_models_3_x(self): + def obsolete_test_load_old_models_pre_3_3(self): """Test loading 3.x models""" old_versions = [ - '3.0.0', '3.1.0', '3.2.0', '3.3.0', '3.4.0' + '3.2.0', '3.1.0', '3.0.0' + ] + for old_version in old_versions: + self._check_old_version(old_version) + + def obsolete_test_load_old_models_post_3_2(self): + """Test loading 3.x models""" + old_versions = [ + '3.4.0', '3.3.0', ] for old_version in old_versions: self._check_old_version(old_version) @@ -201,12 +209,12 @@ def _check_old_version(self, old_version): def testDoc2vecTrainParameters(self): model = doc2vec.Doc2Vec(vector_size=50) - model.build_vocab(documents=list_corpus) + model.build_vocab(corpus_iterable=list_corpus) self.assertRaises(TypeError, model.train, corpus_file=11111) - self.assertRaises(TypeError, model.train, documents=11111) - self.assertRaises(TypeError, model.train, documents=sentences, corpus_file='test') - self.assertRaises(TypeError, model.train, documents=None, corpus_file=None) + self.assertRaises(TypeError, model.train, corpus_iterable=11111) + self.assertRaises(TypeError, model.train, corpus_iterable=sentences, corpus_file='test') + self.assertRaises(TypeError, model.train, corpus_iterable=None, corpus_file=None) self.assertRaises(TypeError, model.train, corpus_file=sentences) @unittest.skipIf(os.name == 'nt', "See another test for Windows below") @@ -418,10 +426,10 @@ def model_sanity(self, model, keep_training=True): # keep training after save if keep_training: - tmpf = get_tmpfile('gensim_doc2vec.tst') + tmpf = get_tmpfile('gensim_doc2vec_resave.tst') model.save(tmpf) loaded = doc2vec.Doc2Vec.load(tmpf) - loaded.train(documents=sentences, total_examples=loaded.corpus_count, epochs=loaded.epochs) + loaded.train(corpus_iterable=sentences, total_examples=loaded.corpus_count, epochs=loaded.epochs) def test_training(self): """Test doc2vec training.""" @@ -440,7 +448,7 @@ def test_training(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_training_fromfile(self): """Test doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec(vector_size=100, min_count=2, epochs=20, workers=1) @@ -461,7 +469,7 @@ def test_dbow_hs(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dbow_hs_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec(corpus_file=corpus_file, dm=0, hs=1, negative=0, min_count=2, epochs=20) self.model_sanity(model) @@ -477,7 +485,7 @@ def test_dmm_hs(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dmm_hs_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec( list_corpus, dm=1, dm_mean=1, vector_size=24, window=4, @@ -496,7 +504,7 @@ def test_dms_hs(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dms_hs_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec( list_corpus, dm=1, dm_mean=0, vector_size=24, window=4, hs=1, @@ -515,7 +523,7 @@ def test_dmc_hs(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dmc_hs_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec( list_corpus, dm=1, dm_concat=1, vector_size=24, window=4, @@ -531,7 +539,7 @@ def test_dbow_neg(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dbow_neg_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec(list_corpus, dm=0, hs=0, negative=10, min_count=2, epochs=20) self.model_sanity(model) @@ -547,7 +555,7 @@ def test_dmm_neg(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dmm_neg_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec( list_corpus, dm=1, dm_mean=1, vector_size=24, window=4, hs=0, @@ -566,7 +574,7 @@ def test_dms_neg(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dms_neg_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec( list_corpus, dm=1, dm_mean=0, vector_size=24, window=4, hs=0, @@ -585,7 +593,7 @@ def test_dmc_neg(self): @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") def test_dmc_neg_fromfile(self): """Test DBOW doc2vec training.""" - with temporary_file(get_tmpfile('gensim_word2vec.tst')) as corpus_file: + with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) model = doc2vec.Doc2Vec( list_corpus, dm=1, dm_concat=1, vector_size=24, window=4, hs=0, @@ -641,9 +649,9 @@ def models_equal(self, model, model2): self.assertEqual(len(model.wv.vocab), len(model2.wv.vocab)) self.assertTrue(np.allclose(model.wv.vectors, model2.wv.vectors)) if model.hs: - self.assertTrue(np.allclose(model.trainables.syn1, model2.trainables.syn1)) + self.assertTrue(np.allclose(model.syn1, model2.syn1)) if model.negative: - self.assertTrue(np.allclose(model.trainables.syn1neg, model2.trainables.syn1neg)) + self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) # check docvecs self.assertEqual(len(model.docvecs.map), len(model2.docvecs.map)) self.assertEqual(len(model.docvecs.index2key), len(model2.docvecs.index2key)) diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index bf602deda1..26fffa89bb 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -16,14 +16,11 @@ from gensim import utils from gensim.models.word2vec import LineSentence -from gensim.models.fasttext import FastText as FT_gensim, _unpack, _unpack_copy -from gensim.models.wrappers.fasttext import FastTextKeyedVectors -from gensim.models.wrappers.fasttext import FastText as FT_wrapper +from gensim.models.fasttext import FastText as FT_gensim, FastTextKeyedVectors, _unpack, _unpack_copy from gensim.models.keyedvectors import KeyedVectors from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences import gensim.models._fasttext_bin from gensim.models.fasttext_inner import compute_ngrams, compute_ngrams_bytes, ft_hash_broken, ft_hash_bytes -from gensim.models.fasttext import _unpack, _unpack_copy import gensim.models.fasttext @@ -73,7 +70,7 @@ def setUp(self): self.test_new_model_file = datapath('lee_fasttext_new.bin') def test_training(self): - model = FT_gensim(size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model = FT_gensim(vector_size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) model.build_vocab(sentences) self.model_sanity(model) @@ -93,7 +90,7 @@ def test_training(self): self.assertEqual(sims, sims2) # build vocab and train in one step; must be the same as above - model2 = FT_gensim(sentences, size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model2 = FT_gensim(sentences, vector_size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) self.models_equal(model, model2) # verify oov-word vector retrieval @@ -105,20 +102,20 @@ def test_training(self): def testFastTextTrainParameters(self): - model = FT_gensim(size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) - model.build_vocab(sentences=sentences) + model = FT_gensim(vector_size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model.build_vocab(corpus_iterable=sentences) self.assertRaises(TypeError, model.train, corpus_file=11111) - self.assertRaises(TypeError, model.train, sentences=11111) - self.assertRaises(TypeError, model.train, sentences=sentences, corpus_file='test') - self.assertRaises(TypeError, model.train, sentences=None, corpus_file=None) + self.assertRaises(TypeError, model.train, corpus_iterable=11111) + self.assertRaises(TypeError, model.train, corpus_iterable=sentences, corpus_file='test') + self.assertRaises(TypeError, model.train, corpus_iterable=None, corpus_file=None) self.assertRaises(TypeError, model.train, corpus_file=sentences) def test_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: utils.save_as_line_sentence(sentences, corpus_file) - model = FT_gensim(size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) + model = FT_gensim(vector_size=12, min_count=1, hs=1, negative=0, seed=42, workers=1, bucket=BUCKET) model.build_vocab(corpus_file=corpus_file) self.model_sanity(model) @@ -151,9 +148,9 @@ def models_equal(self, model, model2): self.assertTrue(np.allclose(model.wv.vectors_ngrams, model2.wv.vectors_ngrams)) self.assertTrue(np.allclose(model.wv.vectors, model2.wv.vectors)) if model.hs: - self.assertTrue(np.allclose(model.trainables.syn1, model2.trainables.syn1)) + self.assertTrue(np.allclose(model.syn1, model2.syn1)) if model.negative: - self.assertTrue(np.allclose(model.trainables.syn1neg, model2.trainables.syn1neg)) + self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) most_common_word = max(model.wv.vocab.items(), key=lambda item: item[1].count)[0] self.assertTrue(np.allclose(model.wv[most_common_word], model2.wv[most_common_word])) @@ -244,12 +241,12 @@ def test_load_fasttext_format(self): actual_vec_oov = model.wv["rejection"] self.assertTrue(np.allclose(actual_vec_oov, expected_vec_oov, atol=1e-4)) - self.assertEqual(model.vocabulary.min_count, 5) + self.assertEqual(model.min_count, 5) self.assertEqual(model.window, 5) self.assertEqual(model.epochs, 5) self.assertEqual(model.negative, 5) - self.assertEqual(model.vocabulary.sample, 0.0001) - self.assertEqual(model.trainables.bucket, 1000) + self.assertEqual(model.sample, 0.0001) + self.assertEqual(model.bucket, 1000) self.assertEqual(model.wv.max_n, 6) self.assertEqual(model.wv.min_n, 3) self.assertEqual(model.wv.vectors.shape, (len(model.wv.vocab), model.vector_size)) @@ -297,12 +294,12 @@ def test_load_fasttext_new_format(self): actual_vec_oov = new_model.wv["rejection"] self.assertTrue(np.allclose(actual_vec_oov, expected_vec_oov, atol=1e-4)) - self.assertEqual(new_model.vocabulary.min_count, 5) + self.assertEqual(new_model.min_count, 5) self.assertEqual(new_model.window, 5) self.assertEqual(new_model.epochs, 5) self.assertEqual(new_model.negative, 5) - self.assertEqual(new_model.vocabulary.sample, 0.0001) - self.assertEqual(new_model.trainables.bucket, 1000) + self.assertEqual(new_model.sample, 0.0001) + self.assertEqual(new_model.bucket, 1000) self.assertEqual(new_model.wv.max_n, 6) self.assertEqual(new_model.wv.min_n, 3) self.assertEqual(new_model.wv.vectors.shape, (len(new_model.wv.vocab), new_model.vector_size)) @@ -406,8 +403,8 @@ def test_wm_distance(self): def test_cbow_hs_training(self): model_gensim = FT_gensim( - size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -435,8 +432,8 @@ def test_cbow_hs_training(self): def test_cbow_hs_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) lee_data = LineSentence(datapath('lee_background.cor')) @@ -468,8 +465,8 @@ def test_cbow_hs_training_fromfile(self): def test_sg_hs_training(self): model_gensim = FT_gensim( - size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -497,8 +494,8 @@ def test_sg_hs_training(self): def test_sg_hs_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -530,8 +527,8 @@ def test_sg_hs_training_fromfile(self): def test_cbow_neg_training(self): model_gensim = FT_gensim( - size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -559,8 +556,8 @@ def test_cbow_neg_training(self): def test_cbow_neg_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -592,8 +589,8 @@ def test_cbow_neg_training_fromfile(self): def test_sg_neg_training(self): model_gensim = FT_gensim( - size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) lee_data = LineSentence(datapath('lee_background.cor')) @@ -621,8 +618,8 @@ def test_sg_neg_training(self): def test_sg_neg_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( - size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) lee_data = LineSentence(datapath('lee_background.cor')) @@ -652,7 +649,7 @@ def test_sg_neg_training_fromfile(self): self.assertGreaterEqual(overlap_count, 2) def test_online_learning(self): - model_hs = FT_gensim(sentences, size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) + model_hs = FT_gensim(sentences, vector_size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) self.assertTrue(len(model_hs.wv.vocab), 12) self.assertTrue(model_hs.wv.vocab['graph'].count, 3) model_hs.build_vocab(new_sentences, update=True) # update vocab @@ -667,7 +664,7 @@ def test_online_learning_fromfile(self): utils.save_as_line_sentence(new_sentences, new_corpus_file) model_hs = FT_gensim( - corpus_file=corpus_file, size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) + corpus_file=corpus_file, vector_size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) self.assertTrue(len(model_hs.wv.vocab), 12) self.assertTrue(model_hs.wv.vocab['graph'].count, 3) model_hs.build_vocab(corpus_file=new_corpus_file, update=True) # update vocab @@ -677,7 +674,7 @@ def test_online_learning_fromfile(self): def test_online_learning_after_save(self): tmpf = get_tmpfile('gensim_fasttext.tst') - model_neg = FT_gensim(sentences, size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) + model_neg = FT_gensim(sentences, vector_size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) model_neg.save(tmpf) model_neg = FT_gensim.load(tmpf) self.assertTrue(len(model_neg.wv.vocab), 12) @@ -693,7 +690,7 @@ def test_online_learning_after_save_fromfile(self): tmpf = get_tmpfile('gensim_fasttext.tst') model_neg = FT_gensim( - corpus_file=corpus_file, size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) + corpus_file=corpus_file, vector_size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) model_neg.save(tmpf) model_neg = FT_gensim.load(tmpf) self.assertTrue(len(model_neg.wv.vocab), 12) @@ -728,16 +725,16 @@ def online_sanity(self, model): self.assertLess(0., sim) def test_sg_hs_online(self): - model = FT_gensim(sg=1, window=2, hs=1, negative=0, min_count=3, iter=1, seed=42, workers=1, bucket=BUCKET) + model = FT_gensim(sg=1, window=2, hs=1, negative=0, min_count=3, epochs=1, seed=42, workers=1, bucket=BUCKET) self.online_sanity(model) def test_sg_neg_online(self): - model = FT_gensim(sg=1, window=2, hs=0, negative=5, min_count=3, iter=1, seed=42, workers=1, bucket=BUCKET) + model = FT_gensim(sg=1, window=2, hs=0, negative=5, min_count=3, epochs=1, seed=42, workers=1, bucket=BUCKET) self.online_sanity(model) def test_cbow_hs_online(self): model = FT_gensim( - sg=0, cbow_mean=1, alpha=0.05, window=2, hs=1, negative=0, min_count=3, iter=1, seed=42, workers=1, + sg=0, cbow_mean=1, alpha=0.05, window=2, hs=1, negative=0, min_count=3, epochs=1, seed=42, workers=1, bucket=BUCKET, ) self.online_sanity(model) @@ -745,12 +742,12 @@ def test_cbow_hs_online(self): def test_cbow_neg_online(self): model = FT_gensim( sg=0, cbow_mean=1, alpha=0.05, window=2, hs=0, negative=5, - min_count=5, iter=1, seed=42, workers=1, sample=0, bucket=BUCKET + min_count=5, epochs=1, seed=42, workers=1, sample=0, bucket=BUCKET ) self.online_sanity(model) def test_get_vocab_word_vecs(self): - model = FT_gensim(size=12, min_count=1, seed=42, bucket=BUCKET) + model = FT_gensim(vector_size=12, min_count=1, seed=42, bucket=BUCKET) model.build_vocab(sentences) original_syn0_vocab = np.copy(model.wv.vectors_vocab) model.wv.adjust_vectors() @@ -759,21 +756,21 @@ def test_get_vocab_word_vecs(self): def test_persistence_word2vec_format(self): """Test storing/loading the model in word2vec format.""" tmpf = get_tmpfile('gensim_fasttext_w2v_format.tst') - model = FT_gensim(sentences, min_count=1, size=12, bucket=BUCKET) + model = FT_gensim(sentences, min_count=1, vector_size=12, bucket=BUCKET) model.wv.save_word2vec_format(tmpf, binary=True) loaded_model_kv = KeyedVectors.load_word2vec_format(tmpf, binary=True) self.assertEqual(len(model.wv.vocab), len(loaded_model_kv.vocab)) self.assertTrue(np.allclose(model.wv['human'], loaded_model_kv['human'])) def test_bucket_ngrams(self): - model = FT_gensim(size=12, min_count=1, bucket=20) + model = FT_gensim(vector_size=12, min_count=1, bucket=20) model.build_vocab(sentences) self.assertEqual(model.wv.vectors_ngrams.shape, (20, 12)) model.build_vocab(new_sentences, update=True) self.assertEqual(model.wv.vectors_ngrams.shape, (20, 12)) def test_estimate_memory(self): - model = FT_gensim(sg=1, hs=1, size=12, negative=5, min_count=3, bucket=BUCKET) + model = FT_gensim(sg=1, hs=1, vector_size=12, negative=5, min_count=3, bucket=BUCKET) model.build_vocab(sentences) report = model.estimate_memory() self.assertEqual(report['vocab'], 2800) @@ -784,8 +781,7 @@ def test_estimate_memory(self): self.assertEqual(report['buckets_word'], 640) self.assertEqual(report['total'], 6704) - @unittest.skipIf(IS_WIN32, "avoid memory error with Appveyor x32") - def testLoadOldModel(self): + def obsolete_testLoadOldModel(self): """Test loading fasttext models from previous version""" model_file = 'fasttext_old' @@ -794,9 +790,9 @@ def testLoadOldModel(self): self.assertTrue(len(model.wv.vocab) == 12) self.assertTrue(len(model.wv.index2word) == 12) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.trainables.vectors_lockf.shape == (12, )) - self.assertTrue(model.vocabulary.cum_table.shape == (12, )) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.wv.vectors_lockf.shape == (12, )) + self.assertTrue(model.cum_table.shape == (12, )) self.assertEqual(model.wv.vectors_vocab.shape, (12, 100)) self.assertEqual(model.wv.vectors_ngrams.shape, (2000000, 100)) @@ -808,9 +804,9 @@ def testLoadOldModel(self): self.assertTrue(len(model.wv.vocab) == 12) self.assertTrue(len(model.wv.index2word) == 12) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.trainables.vectors_lockf.shape == (12, )) - self.assertTrue(model.vocabulary.cum_table.shape == (12, )) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.wv.vectors_lockf.shape == (12, )) + self.assertTrue(model.cum_table.shape == (12, )) self.assertEqual(model.wv.vectors_vocab.shape, (12, 100)) self.assertEqual(model.wv.vectors_ngrams.shape, (2000000, 100)) @@ -833,13 +829,13 @@ def compare_with_wrapper(self, model_gensim, model_wrapper): def test_cbow_hs_against_wrapper(self): tmpf = get_tmpfile('gensim_fasttext.tst') model_wrapper = FT_wrapper.train(ft_path=FT_CMD, corpus_file=datapath('lee_background.cor'), - output_file=tmpf, model='cbow', size=50, alpha=0.05, window=5, min_count=5, + output_file=tmpf, model='cbow', vector_size=50, alpha=0.05, window=5, min_count=5, word_ngrams=1, - loss='hs', sample=1e-3, negative=0, iter=5, min_n=3, max_n=6, sorted_vocab=1, + loss='hs', sample=1e-3, negative=0, epochs=5, min_n=3, max_n=6, sorted_vocab=1, threads=12) - model_gensim = FT_gensim(size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + model_gensim = FT_gensim(vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -854,13 +850,13 @@ def test_sg_hs_against_wrapper(self): tmpf = get_tmpfile('gensim_fasttext.tst') model_wrapper = FT_wrapper.train(ft_path=FT_CMD, corpus_file=datapath('lee_background.cor'), - output_file=tmpf, model='skipgram', size=50, alpha=0.025, window=5, + output_file=tmpf, model='skipgram', vector_size=48, alpha=0.025, window=5, min_count=5, word_ngrams=1, - loss='hs', sample=1e-3, negative=0, iter=5, min_n=3, max_n=6, sorted_vocab=1, + loss='hs', sample=1e-3, negative=0, epochs=5, min_n=3, max_n=6, sorted_vocab=1, threads=12) - model_gensim = FT_gensim(size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, - min_count=5, iter=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + model_gensim = FT_gensim(vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, + min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -879,7 +875,7 @@ def train_gensim(bucket=100, min_count=5): # # Set parameters to match those in the load_native function # - model = FT_gensim(bucket=bucket, size=5, alpha=0.05, workers=1, sample=0.0001, min_count=min_count) + model = FT_gensim(bucket=bucket, vector_size=5, alpha=0.05, workers=1, sample=0.0001, min_count=min_count) model.build_vocab(TOY_SENTENCES) model.train(TOY_SENTENCES, total_examples=len(TOY_SENTENCES), epochs=model.epochs) return model @@ -1035,8 +1031,8 @@ def test_sanity(self): # self.assertEqual(trained.bucket, native.bucket) compare_wv(trained.wv, native.wv, self) - compare_vocabulary(trained.vocabulary, native.vocabulary, self) - compare_nn(trained.trainables, native.trainables, self) + compare_vocabulary(trained, native, self) + compare_nn(trained, native, self) def test_continuation_native(self): """Ensure that training has had a measurable effect.""" @@ -1159,7 +1155,7 @@ class HashCompatibilityTest(unittest.TestCase): def test_compatibility_true(self): m = FT_gensim.load(datapath('compatible-hash-true.model')) self.assertTrue(m.wv.compatible_hash) - self.assertEqual(m.trainables.bucket, m.wv.bucket) + self.assertEqual(m.bucket, m.wv.bucket) def test_compatibility_false(self): # @@ -1167,12 +1163,12 @@ def test_compatibility_false(self): # m = FT_gensim.load(datapath('compatible-hash-false.model')) self.assertFalse(m.wv.compatible_hash) - self.assertEqual(m.trainables.bucket, m.wv.bucket) + self.assertEqual(m.bucket, m.wv.bucket) def test_hash_native(self): m = load_native() self.assertTrue(m.wv.compatible_hash) - self.assertEqual(m.trainables.bucket, m.wv.bucket) + self.assertEqual(m.bucket, m.wv.bucket) class FTHashResultsTest(unittest.TestCase): @@ -1581,7 +1577,7 @@ class SaveFacebookFormatModelTest(unittest.TestCase): def _check_roundtrip(self, sg): model_params = { "sg": sg, - "size": 10, + "vector_size": 10, "min_count": 1, "hs": 1, "negative": 5, @@ -1636,7 +1632,7 @@ class SaveGensimByteIdentityTest(unittest.TestCase): def _check_roundtrip_file_file(self, sg): model_params = { "sg": sg, - "size": 10, + "vector_size": 10, "min_count": 1, "hs": 1, "negative": 0, @@ -1665,7 +1661,7 @@ def _save_test_model(out_base_fname, model_params): inp_fname = datapath('lee_background.cor') model_type = "cbow" if model_params["sg"] == 0 else "skipgram" - size = str(model_params["size"]) + size = str(model_params["vector_size"]) seed = str(model_params["seed"]) cmd = [ @@ -1687,7 +1683,7 @@ class SaveFacebookByteIdentityTest(unittest.TestCase): """ def _check_roundtrip_file_file(self, sg): - model_params = {"size": 10, "sg": sg, "seed": 42} + model_params = {"vector_size": 10, "sg": sg, "seed": 42} # fasttext tool creates both *vec and *bin files, so we have to remove both, even thought *vec is unused @@ -1736,7 +1732,7 @@ class SaveFacebookFormatReadingTest(unittest.TestCase): def _check_load_fasttext_format(self, sg): model_params = { "sg": sg, - "size": 10, + "vector_size": 10, "min_count": 1, "hs": 1, "negative": 5, diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py deleted file mode 100644 index 9c619a3efb..0000000000 --- a/gensim/test/test_fasttext_wrapper.py +++ /dev/null @@ -1,382 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Copyright (C) 2010 Radim Rehurek -# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html - -""" -Automated tests for checking transformation algorithms (the models package). -""" - -import logging -import unittest -import os - -import numpy - -from gensim.models.wrappers import fasttext -from gensim.models import keyedvectors -from gensim.test.utils import datapath, get_tmpfile - - -try: - from pyemd import emd # noqa:F401 - PYEMD_EXT = True -except (ImportError, ValueError): - PYEMD_EXT = False - - -logger = logging.getLogger(__name__) - - -class TestFastText(unittest.TestCase): - def setUp(self): - ft_home = os.environ.get('FT_HOME', None) - self.ft_path = os.path.join(ft_home, 'fasttext') if ft_home else None - self.corpus_file = datapath('lee_background.cor') - self.test_model_file = datapath('lee_fasttext') - self.test_new_model_file = datapath('lee_fasttext_new') - # Load pre-trained model to perform tests in case FastText binary isn't available in test environment - self.test_model = fasttext.FastText.load_fasttext_format(self.test_model_file) - - def model_sanity(self, model): - """Even tiny models trained on any corpus should pass these sanity checks""" - self.assertEqual(model.wv.syn0.shape, (len(model.wv.vocab), model.vector_size)) - self.assertEqual(model.wv.syn0_ngrams.shape, (model.num_ngram_vectors, model.vector_size)) - - def models_equal(self, model1, model2): - self.assertEqual(len(model1.wv.vocab), len(model2.wv.vocab)) - self.assertEqual(set(model1.wv.vocab.keys()), set(model2.wv.vocab.keys())) - self.assertTrue(numpy.allclose(model1.wv.syn0, model2.wv.syn0)) - self.assertTrue(numpy.allclose(model1.wv.syn0_ngrams, model2.wv.syn0_ngrams)) - - def testTraining(self): - """Test self.test_model successfully trained, parameters and weights correctly loaded""" - if self.ft_path is None: - logger.info("FT_HOME env variable not set, skipping test") - return # Use self.skipTest once python < 2.7 is no longer supported - vocab_size, model_size = 1763, 10 - tmpf = get_tmpfile('gensim_fasttext_wrapper.tst') - trained_model = fasttext.FastText.train( - self.ft_path, self.corpus_file, size=model_size, output_file=tmpf - ) - - self.assertEqual(trained_model.wv.syn0.shape, (vocab_size, model_size)) - self.assertEqual(len(trained_model.wv.vocab), vocab_size) - self.assertEqual(trained_model.wv.syn0_ngrams.shape[1], model_size) - self.model_sanity(trained_model) - - # Tests temporary training files deleted - self.assertFalse(os.path.exists('%s.bin' % tmpf)) - - def testMinCount(self): - """Tests words with frequency less than `min_count` absent from vocab""" - if self.ft_path is None: - logger.info("FT_HOME env variable not set, skipping test") - return # Use self.skipTest once python < 2.7 is no longer supported - tmpf = get_tmpfile('gensim_fasttext_wrapper.tst') - test_model_min_count_5 = fasttext.FastText.train( - self.ft_path, self.corpus_file, output_file=tmpf, size=10, min_count=5 - ) - self.assertTrue('forests' not in test_model_min_count_5.wv.vocab) - - test_model_min_count_1 = fasttext.FastText.train( - self.ft_path, self.corpus_file, output_file=tmpf, size=10, min_count=1 - ) - self.assertTrue('forests' in test_model_min_count_1.wv.vocab) - - def testModelSize(self): - """Tests output vector dimensions are the same as the value for `size` param""" - if self.ft_path is None: - logger.info("FT_HOME env variable not set, skipping test") - return # Use self.skipTest once python < 2.7 is no longer supported - tmpf = get_tmpfile('gensim_fasttext_wrapper.tst') - test_model_size_20 = fasttext.FastText.train( - self.ft_path, self.corpus_file, output_file=tmpf, size=20 - ) - self.assertEqual(test_model_size_20.vector_size, 20) - self.assertEqual(test_model_size_20.wv.syn0.shape[1], 20) - self.assertEqual(test_model_size_20.wv.syn0_ngrams.shape[1], 20) - - def testPersistence(self): - """Test storing/loading the entire model.""" - tmpf = get_tmpfile('gensim_fasttext_wrapper.tst') - self.test_model.save(tmpf) - loaded = fasttext.FastText.load(tmpf) - self.models_equal(self.test_model, loaded) - - self.test_model.save(tmpf, sep_limit=0) - self.models_equal(self.test_model, fasttext.FastText.load(tmpf)) - - def testNormalizedVectorsNotSaved(self): - """Test syn0norm/syn0_ngrams_norm aren't saved in model file""" - tmpf = get_tmpfile('gensim_fasttext_wrapper.tst') - self.test_model.init_sims() - self.test_model.save(tmpf) - loaded = fasttext.FastText.load(tmpf) - self.assertTrue(loaded.wv.syn0norm is None) - self.assertTrue(loaded.wv.syn0_ngrams_norm is None) - - wv = self.test_model.wv - wv.save(tmpf) - loaded_kv = keyedvectors.KeyedVectors.load(tmpf) - self.assertTrue(loaded_kv.syn0norm is None) - self.assertTrue(loaded_kv.syn0_ngrams_norm is None) - - def testLoadFastTextFormat(self): - """Test model successfully loaded from fastText .bin file""" - try: - model = fasttext.FastText.load_fasttext_format(self.test_model_file) - except Exception as exc: - self.fail('Unable to load FastText model from file %s: %s' % (self.test_model_file, exc)) - vocab_size, model_size = 1762, 10 - self.assertEqual(model.wv.syn0.shape, (vocab_size, model_size)) - self.assertEqual(len(model.wv.vocab), vocab_size, model_size) - self.assertEqual(model.wv.syn0_ngrams.shape, (model.num_ngram_vectors, model_size)) - - expected_vec = [ - -0.57144, - -0.0085561, - 0.15748, - -0.67855, - -0.25459, - -0.58077, - -0.09913, - 1.1447, - 0.23418, - 0.060007 - ] # obtained using ./fasttext print-word-vectors lee_fasttext_new.bin - self.assertTrue(numpy.allclose(model["hundred"], expected_vec, atol=1e-4)) - - # vector for oov words are slightly different from original FastText due to discarding unused ngrams - # obtained using a modified version of ./fasttext print-word-vectors lee_fasttext_new.bin - expected_vec_oov = [ - -0.23825, - -0.58482, - -0.22276, - -0.41215, - 0.91015, - -1.6786, - -0.26724, - 0.58818, - 0.57828, - 0.75801 - ] - self.assertTrue(numpy.allclose(model["rejection"], expected_vec_oov, atol=1e-4)) - - self.assertEqual(model.min_count, 5) - self.assertEqual(model.window, 5) - self.assertEqual(model.iter, 5) - self.assertEqual(model.negative, 5) - self.assertEqual(model.sample, 0.0001) - self.assertEqual(model.bucket, 1000) - self.assertEqual(model.wv.max_n, 6) - self.assertEqual(model.wv.min_n, 3) - self.model_sanity(model) - - def testLoadFastTextNewFormat(self): - """ Test model successfully loaded from fastText (new format) .bin file """ - try: - new_model = fasttext.FastText.load_fasttext_format(self.test_new_model_file) - except Exception as exc: - self.fail('Unable to load FastText model from file %s: %s' % (self.test_new_model_file, exc)) - vocab_size, model_size = 1763, 10 - self.assertEqual(new_model.wv.syn0.shape, (vocab_size, model_size)) - self.assertEqual(len(new_model.wv.vocab), vocab_size, model_size) - self.assertEqual(new_model.wv.syn0_ngrams.shape, (new_model.num_ngram_vectors, model_size)) - - expected_vec = [ - -0.025627, - -0.11448, - 0.18116, - -0.96779, - 0.2532, - -0.93224, - 0.3929, - 0.12679, - -0.19685, - -0.13179 - ] # obtained using ./fasttext print-word-vectors lee_fasttext_new.bin - self.assertTrue(numpy.allclose(new_model["hundred"], expected_vec, atol=1e-4)) - - # vector for oov words are slightly different from original FastText due to discarding unused ngrams - # obtained using a modified version of ./fasttext print-word-vectors lee_fasttext_new.bin - expected_vec_oov = [ - -0.53378, - -0.19, - 0.013482, - -0.86767, - -0.21684, - -0.89928, - 0.45124, - 0.18025, - -0.14128, - 0.22508 - ] - self.assertTrue(numpy.allclose(new_model["rejection"], expected_vec_oov, atol=1e-4)) - - self.assertEqual(new_model.min_count, 5) - self.assertEqual(new_model.window, 5) - self.assertEqual(new_model.iter, 5) - self.assertEqual(new_model.negative, 5) - self.assertEqual(new_model.sample, 0.0001) - self.assertEqual(new_model.bucket, 1000) - self.assertEqual(new_model.wv.max_n, 6) - self.assertEqual(new_model.wv.min_n, 3) - self.model_sanity(new_model) - - def testLoadFileName(self): - """ Test model accepts input as both `/path/to/model` or `/path/to/model.bin` """ - self.assertTrue(fasttext.FastText.load_fasttext_format(datapath('lee_fasttext_new'))) - self.assertTrue(fasttext.FastText.load_fasttext_format(datapath('lee_fasttext_new.bin'))) - - def testLoadModelSupervised(self): - """Test loading model with supervised learning labels""" - with self.assertRaises(NotImplementedError): - fasttext.FastText.load_fasttext_format(datapath('pang_lee_polarity_fasttext')) - - def testLoadModelWithNonAsciiVocab(self): - """Test loading model with non-ascii words in vocab""" - model = fasttext.FastText.load_fasttext_format(datapath('non_ascii_fasttext')) - self.assertTrue(u'který' in model) - try: - vector = model[u'který'] # noqa:F841 - except UnicodeDecodeError: - self.fail('Unable to access vector for utf8 encoded non-ascii word') - - def testLoadModelNonUtf8Encoding(self): - """Test loading model with words in user-specified encoding""" - model = fasttext.FastText.load_fasttext_format(datapath('cp852_fasttext'), encoding='cp852') - self.assertTrue(u'který' in model) - try: - vector = model[u'který'] # noqa:F841 - except KeyError: - self.fail('Unable to access vector for cp-852 word') - - def testNSimilarity(self): - """Test n_similarity for in-vocab and out-of-vocab words""" - # In vocab, sanity check - self.assertTrue(numpy.allclose(self.test_model.n_similarity(['the', 'and'], ['and', 'the']), 1.0)) - self.assertEqual(self.test_model.n_similarity(['the'], ['and']), self.test_model.n_similarity(['and'], ['the'])) - # Out of vocab check - self.assertTrue(numpy.allclose(self.test_model.n_similarity(['night', 'nights'], ['nights', 'night']), 1.0)) - self.assertEqual( - self.test_model.n_similarity(['night'], ['nights']), - self.test_model.n_similarity(['nights'], ['night']) - ) - - def testSimilarity(self): - """Test similarity for in-vocab and out-of-vocab words""" - # In vocab, sanity check - self.assertTrue(numpy.allclose(self.test_model.similarity('the', 'the'), 1.0)) - self.assertEqual(self.test_model.similarity('the', 'and'), self.test_model.similarity('and', 'the')) - # Out of vocab check - self.assertTrue(numpy.allclose(self.test_model.similarity('nights', 'nights'), 1.0)) - self.assertEqual(self.test_model.similarity('night', 'nights'), self.test_model.similarity('nights', 'night')) - - def testMostSimilar(self): - """Test most_similar for in-vocab and out-of-vocab words""" - # In vocab, sanity check - self.assertEqual(len(self.test_model.most_similar(positive=['the', 'and'], topn=5)), 5) - self.assertEqual(self.test_model.most_similar('the'), self.test_model.most_similar(positive=['the'])) - # Out of vocab check - self.assertEqual(len(self.test_model.most_similar(['night', 'nights'], topn=5)), 5) - self.assertEqual(self.test_model.most_similar('nights'), self.test_model.most_similar(positive=['nights'])) - - def testMostSimilarCosmul(self): - """Test most_similar_cosmul for in-vocab and out-of-vocab words""" - # In vocab, sanity check - self.assertEqual(len(self.test_model.most_similar_cosmul(positive=['the', 'and'], topn=5)), 5) - self.assertEqual( - self.test_model.most_similar_cosmul('the'), - self.test_model.most_similar_cosmul(positive=['the'])) - # Out of vocab check - self.assertEqual(len(self.test_model.most_similar_cosmul(['night', 'nights'], topn=5)), 5) - self.assertEqual( - self.test_model.most_similar_cosmul('nights'), - self.test_model.most_similar_cosmul(positive=['nights'])) - - def testLookup(self): - """Tests word vector lookup for in-vocab and out-of-vocab words""" - # In vocab, sanity check - self.assertTrue('night' in self.test_model.wv.vocab) - self.assertTrue(numpy.allclose(self.test_model['night'], self.test_model[['night']])) - # Out of vocab check - self.assertFalse('nights' in self.test_model.wv.vocab) - self.assertTrue(numpy.allclose(self.test_model['nights'], self.test_model[['nights']])) - # Word with no ngrams in model - self.assertRaises(KeyError, lambda: self.test_model['a!@']) - - def testContains(self): - """Tests __contains__ for in-vocab and out-of-vocab words""" - # In vocab, sanity check - self.assertTrue('night' in self.test_model.wv.vocab) - self.assertTrue('night' in self.test_model) - # Out of vocab check - self.assertFalse('nights' in self.test_model.wv.vocab) - self.assertTrue('nights' in self.test_model) - # Word with no ngrams in model - self.assertFalse('a!@' in self.test_model.wv.vocab) - self.assertFalse('a!@' in self.test_model) - - @unittest.skipIf(PYEMD_EXT is False, "pyemd not installed") - def testWmdistance(self): - """Tests wmdistance for docs with in-vocab and out-of-vocab words""" - doc = ['night', 'payment'] - oov_doc = ['nights', 'forests', 'payments'] - ngrams_absent_doc = ['a!@', 'b#$'] - - dist = self.test_model.wmdistance(doc, oov_doc) - self.assertNotEqual(float('inf'), dist) - dist = self.test_model.wmdistance(doc, ngrams_absent_doc) - self.assertEqual(float('inf'), dist) - - def testDoesntMatch(self): - """Tests doesnt_match for list of out-of-vocab words""" - oov_words = ['nights', 'forests', 'payments'] - # Out of vocab check - for word in oov_words: - self.assertFalse(word in self.test_model.wv.vocab) - try: - self.test_model.doesnt_match(oov_words) - except Exception: - self.fail('model.doesnt_match raises exception for oov words') - - def testHash(self): - # Tests FastText.ft_hash method return values to those obtained from original C implementation - ft_hash = fasttext.ft_hash('test') - self.assertEqual(ft_hash, 2949673445) - ft_hash = fasttext.ft_hash('word') - self.assertEqual(ft_hash, 1788406269) - - def testConsistentDtype(self): - """Test that the same dtype is returned for OOV words as for words in the vocabulary""" - vocab_word = 'night' - oov_word = 'wordnotpresentinvocabulary' - self.assertIn(vocab_word, self.test_model.wv.vocab) - self.assertNotIn(oov_word, self.test_model.wv.vocab) - - vocab_embedding = self.test_model[vocab_word] - oov_embedding = self.test_model[oov_word] - self.assertEqual(vocab_embedding.dtype, oov_embedding.dtype) - - def testPersistenceForOldVersions(self): - """Test backward compatibility for models saved with versions < 3.0.0""" - old_model_path = datapath('ft_model_2.3.0') - loaded_model = fasttext.FastText.load(old_model_path) - self.assertEqual(loaded_model.vector_size, 10) - self.assertEqual(loaded_model.wv.syn0.shape[1], 10) - self.assertEqual(loaded_model.wv.syn0_ngrams.shape[1], 10) - # in-vocab word - in_expected_vec = numpy.array([-2.44566941, -1.54802394, -2.61103821, -1.88549316, 1.02860415, - 1.19031894, 2.01627707, 1.98942184, -1.39095843, -0.65036952]) - self.assertTrue(numpy.allclose(loaded_model["the"], in_expected_vec, atol=1e-4)) - # out-of-vocab word - out_expected_vec = numpy.array([-1.34948218, -0.8686831, -1.51483142, -1.0164026, 0.56272298, - 0.66228276, 1.06477463, 1.1355902, -0.80972326, -0.39845538]) - self.assertTrue(numpy.allclose(loaded_model["random_word"], out_expected_vec, atol=1e-4)) - - -if __name__ == '__main__': - logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) - unittest.main() diff --git a/gensim/test/test_keras_integration.py b/gensim/test/test_keras_integration.py index bad0bb8b95..3eb2841f58 100644 --- a/gensim/test/test_keras_integration.py +++ b/gensim/test/test_keras_integration.py @@ -25,7 +25,7 @@ class TestKerasWord2VecWrapper(unittest.TestCase): def setUp(self): - self.model_cos_sim = word2vec.Word2Vec(common_texts, size=100, min_count=1, hs=1) + self.model_cos_sim = word2vec.Word2Vec(common_texts, vector_size=100, min_count=1, hs=1) self.model_twenty_ng = word2vec.Word2Vec(min_count=1) def testWord2VecTraining(self): @@ -34,7 +34,7 @@ def testWord2VecTraining(self): """ model = self.model_cos_sim self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 100)) - self.assertTrue(model.trainables.syn1.shape == (len(model.wv.vocab), 100)) + self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 100)) sims = model.wv.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py index 3eb3ac28cc..e56da9a9e4 100644 --- a/gensim/test/test_keyedvectors.py +++ b/gensim/test/test_keyedvectors.py @@ -142,11 +142,11 @@ def test_similarity(self): self.assertTrue(np.allclose(self.vectors.similarity('war', 'war'), 1)) self.assertTrue(np.allclose(self.vectors.similarity('war', 'conflict'), 0.93305397)) - def test_words_closer_than(self): + def test_closer_than(self): """Test words_closer_than returns expected value for distinct and identical nodes.""" - self.assertEqual(self.vectors.words_closer_than('war', 'war'), []) + self.assertEqual(self.vectors.closer_than('war', 'war'), []) expected = set(['conflict', 'administration']) - self.assertEqual(set(self.vectors.words_closer_than('war', 'terrorism')), expected) + self.assertEqual(set(self.vectors.closer_than('war', 'terrorism')), expected) def test_rank(self): """Test rank returns expected value for distinct and identical nodes.""" diff --git a/gensim/test/test_poincare.py b/gensim/test/test_poincare.py index c4fe8af433..f0520d0a7f 100644 --- a/gensim/test/test_poincare.py +++ b/gensim/test/test_poincare.py @@ -383,11 +383,11 @@ def test_difference_in_hierarchy(self): self.assertTrue(np.allclose(self.vectors.difference_in_hierarchy('mammal.n.01', 'dog.n.01'), 0.9384287)) self.assertTrue(np.allclose(self.vectors.difference_in_hierarchy('dog.n.01', 'mammal.n.01'), -0.9384287)) - def test_words_closer_than(self): - """Test words_closer_than returns expected value for distinct and identical nodes.""" - self.assertEqual(self.vectors.words_closer_than('dog.n.01', 'dog.n.01'), []) + def test_closer_than(self): + """Test closer_than returns expected value for distinct and identical nodes.""" + self.assertEqual(self.vectors.closer_than('dog.n.01', 'dog.n.01'), []) expected = set(['canine.n.02', 'hunting_dog.n.01']) - self.assertEqual(set(self.vectors.words_closer_than('dog.n.01', 'carnivore.n.01')), expected) + self.assertEqual(set(self.vectors.closer_than('dog.n.01', 'carnivore.n.01')), expected) def test_rank(self): """Test rank returns expected value for distinct and identical nodes.""" diff --git a/gensim/test/test_sklearn_api.py b/gensim/test/test_sklearn_api.py index e14fef351e..f6a8d26c57 100644 --- a/gensim/test/test_sklearn_api.py +++ b/gensim/test/test_sklearn_api.py @@ -658,7 +658,7 @@ def testModelNotFitted(self): class TestWord2VecWrapper(unittest.TestCase): def setUp(self): numpy.random.seed(0) - self.model = W2VTransformer(size=10, min_count=0, seed=42) + self.model = W2VTransformer(vector_size=10, min_count=0, seed=42) self.model.fit(texts) def testTransform(self): @@ -667,21 +667,21 @@ def testTransform(self): words = words + texts[0] matrix = self.model.transform(words) self.assertEqual(matrix.shape[0], 3) - self.assertEqual(matrix.shape[1], self.model.size) + self.assertEqual(matrix.shape[1], self.model.vector_size) # tranform one word word = texts[0][0] matrix = self.model.transform(word) self.assertEqual(matrix.shape[0], 1) - self.assertEqual(matrix.shape[1], self.model.size) + self.assertEqual(matrix.shape[1], self.model.vector_size) def testConsistencyWithGensimModel(self): # training a W2VTransformer - self.model = W2VTransformer(size=10, min_count=0, seed=42) + self.model = W2VTransformer(vector_size=10, min_count=0, seed=42) self.model.fit(texts) # training a Gensim Word2Vec model with the same params - gensim_w2vmodel = models.Word2Vec(texts, size=10, min_count=0, seed=42) + gensim_w2vmodel = models.Word2Vec(texts, vector_size=10, min_count=0, seed=42) word = texts[0][0] vec_transformer_api = self.model.transform(word) # vector returned by W2VTransformer @@ -691,7 +691,7 @@ def testConsistencyWithGensimModel(self): def testPipeline(self): numpy.random.seed(0) # set fixed seed to get similar values everytime - model = W2VTransformer(size=10, min_count=1) + model = W2VTransformer(vector_size=10, min_count=1) model.fit(w2v_texts) class_dict = {'mathematics': 1, 'physics': 0} @@ -728,7 +728,7 @@ def testPersistence(self): # sanity check for transformation operation self.assertEqual(loaded_transformed_vecs.shape[0], 1) - self.assertEqual(loaded_transformed_vecs.shape[1], model_load.size) + self.assertEqual(loaded_transformed_vecs.shape[1], model_load.vector_size) # comparing the original and loaded models original_transformed_vecs = self.model.transform(word) @@ -736,7 +736,7 @@ def testPersistence(self): self.assertTrue(passed) def testModelNotFitted(self): - w2vmodel_wrapper = W2VTransformer(size=10, min_count=0, seed=42) + w2vmodel_wrapper = W2VTransformer(vector_size=10, min_count=0, seed=42) word = texts[0][0] self.assertRaises(NotFittedError, w2vmodel_wrapper.transform, word) @@ -835,13 +835,13 @@ def testTransform(self): docs = [w2v_texts[0], w2v_texts[1], w2v_texts[2]] matrix = self.model.transform(docs) self.assertEqual(matrix.shape[0], 3) - self.assertEqual(matrix.shape[1], self.model.size) + self.assertEqual(matrix.shape[1], self.model.vector_size) # tranform one document doc = w2v_texts[0] matrix = self.model.transform(doc) self.assertEqual(matrix.shape[0], 1) - self.assertEqual(matrix.shape[1], self.model.size) + self.assertEqual(matrix.shape[1], self.model.vector_size) def testFitTransform(self): model = D2VTransformer(min_count=1) @@ -850,13 +850,13 @@ def testFitTransform(self): docs = [w2v_texts[0], w2v_texts[1], w2v_texts[2]] matrix = model.fit_transform(docs) self.assertEqual(matrix.shape[0], 3) - self.assertEqual(matrix.shape[1], model.size) + self.assertEqual(matrix.shape[1], model.vector_size) # fit and transform one document doc = w2v_texts[0] matrix = model.fit_transform(doc) self.assertEqual(matrix.shape[0], 1) - self.assertEqual(matrix.shape[1], model.size) + self.assertEqual(matrix.shape[1], model.vector_size) def testSetGetParams(self): # updating only one param @@ -896,7 +896,7 @@ def testPersistence(self): # sanity check for transformation operation self.assertEqual(loaded_transformed_vecs.shape[0], 1) - self.assertEqual(loaded_transformed_vecs.shape[1], model_load.size) + self.assertEqual(loaded_transformed_vecs.shape[1], model_load.vector_size) # comparing the original and loaded models original_transformed_vecs = self.model.transform(doc) @@ -1301,9 +1301,9 @@ def testModelNotFitted(self): self.assertRaises(NotFittedError, phrases_transformer.transform, phrases_sentences[0]) -class TestFastTextWrapper(unittest.TestCase): +class TestFTTransformer(unittest.TestCase): def setUp(self): - self.model = FTTransformer(size=10, min_count=0, seed=42, bucket=5000) + self.model = FTTransformer(vector_size=10, min_count=0, seed=42, bucket=5000) self.model.fit(texts) def testTransform(self): @@ -1312,30 +1312,30 @@ def testTransform(self): words = words + texts[0] matrix = self.model.transform(words) self.assertEqual(matrix.shape[0], 3) - self.assertEqual(matrix.shape[1], self.model.size) + self.assertEqual(matrix.shape[1], self.model.vector_size) # tranform one word word = texts[0][0] matrix = self.model.transform(word) self.assertEqual(matrix.shape[0], 1) - self.assertEqual(matrix.shape[1], self.model.size) + self.assertEqual(matrix.shape[1], self.model.vector_size) # verify oov-word vector retrieval invocab_vec = self.model.transform("computer") # invocab word self.assertEqual(invocab_vec.shape[0], 1) - self.assertEqual(invocab_vec.shape[1], self.model.size) + self.assertEqual(invocab_vec.shape[1], self.model.vector_size) oov_vec = self.model.transform('compute') # oov word self.assertEqual(oov_vec.shape[0], 1) - self.assertEqual(oov_vec.shape[1], self.model.size) + self.assertEqual(oov_vec.shape[1], self.model.vector_size) def testConsistencyWithGensimModel(self): # training a FTTransformer - self.model = FTTransformer(size=10, min_count=0, seed=42, workers=1, bucket=5000) + self.model = FTTransformer(vector_size=10, min_count=0, seed=42, workers=1, bucket=5000) self.model.fit(texts) # training a Gensim FastText model with the same params - gensim_ftmodel = models.FastText(texts, size=10, min_count=0, seed=42, workers=1, bucket=5000) + gensim_ftmodel = models.FastText(texts, vector_size=10, min_count=0, seed=42, workers=1, bucket=5000) # vectors returned by FTTransformer vecs_transformer_api = self.model.transform( @@ -1353,7 +1353,7 @@ def testConsistencyWithGensimModel(self): self.assertTrue(passed) def testPipeline(self): - model = FTTransformer(size=10, min_count=1, bucket=5000) + model = FTTransformer(vector_size=10, min_count=1, bucket=5000) model.fit(w2v_texts) class_dict = {'mathematics': 1, 'physics': 0} @@ -1391,7 +1391,7 @@ def testPersistence(self): # sanity check for transformation operation self.assertEqual(loaded_transformed_vecs.shape[0], len(words)) - self.assertEqual(loaded_transformed_vecs.shape[1], model_load.size) + self.assertEqual(loaded_transformed_vecs.shape[1], model_load.vector_size) # comparing the original and loaded models original_transformed_vecs = self.model.transform(words) @@ -1399,7 +1399,7 @@ def testPersistence(self): self.assertTrue(passed) def testModelNotFitted(self): - ftmodel_wrapper = FTTransformer(size=10, min_count=0, seed=42, bucket=5000) + ftmodel_wrapper = FTTransformer(vector_size=10, min_count=0, seed=42, bucket=5000) word = texts[0][0] self.assertRaises(NotFittedError, ftmodel_wrapper.transform, word) diff --git a/gensim/test/test_translation_matrix.py b/gensim/test/test_translation_matrix.py index f6798ac9cc..2841845e6c 100644 --- a/gensim/test/test_translation_matrix.py +++ b/gensim/test/test_translation_matrix.py @@ -2,7 +2,6 @@ # encoding: utf-8 from collections import namedtuple import unittest -import math import logging import numpy as np @@ -92,31 +91,33 @@ def setUp(self): filename = datapath("alldata-id-10.txt") train_docs = read_sentiment_docs(filename) self.train_docs = train_docs - self.source_doc_vec_file = datapath("small_tag_doc_5_iter50") - self.target_doc_vec_file = datapath("large_tag_doc_10_iter50") - - self.source_doc_vec = Doc2Vec.load(self.source_doc_vec_file) - self.target_doc_vec = Doc2Vec.load(self.target_doc_vec_file) + self.source_doc_vec = Doc2Vec(documents=train_docs[:5], vector_size=8, epochs=50, seed=1) + self.target_doc_vec = Doc2Vec(documents=train_docs, vector_size=8, epochs=50, seed=2) def test_translation_matrix(self): model = translation_matrix.BackMappingTranslationMatrix( self.source_doc_vec, self.target_doc_vec, self.train_docs[:5] ) transmat = model.train(self.train_docs[:5]) - self.assertEqual(transmat.shape, (100, 100)) + self.assertEqual(transmat.shape, (8, 8)) def test_infer_vector(self): + """Test that translation gives similar results to traditional inference. + + This may not be completely sensible/salient with such tiny data, but + replaces a nonsensical test. + """ model = translation_matrix.BackMappingTranslationMatrix( self.source_doc_vec, self.target_doc_vec, self.train_docs[:5] ) model.train(self.train_docs[:5]) - infered_vec = model.infer_vector(self.target_doc_vec.docvecs[self.train_docs[5].tags]) - self.assertEqual(infered_vec.shape, (100, )) + backmapped_vec = model.infer_vector(self.target_doc_vec.docvecs[self.train_docs[5].tags]) + self.assertEqual(backmapped_vec.shape, (8, )) + + d2v_inferred_vector = self.source_doc_vec.infer_vector(self.train_docs[5].words) - expected = 0.6453547135 - eps = 1e-6 - caculated = cosine(self.target_doc_vec.docvecs[self.train_docs[5].tags], infered_vec) - self.assertLessEqual(math.fabs(caculated - expected), eps) + distance = cosine(backmapped_vec, d2v_inferred_vector) + self.assertLessEqual(distance, 0.1) if __name__ == '__main__': diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index b522fe4131..28e66bab1c 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -75,8 +75,8 @@ def testBuildVocabFromFreq(self): 'survey': 2, 'user': 3, 'human': 2, 'time': 2, 'interface': 2, 'response': 2 } - model_hs = word2vec.Word2Vec(size=10, min_count=0, seed=42, hs=1, negative=0) - model_neg = word2vec.Word2Vec(size=10, min_count=0, seed=42, hs=0, negative=5) + model_hs = word2vec.Word2Vec(vector_size=10, min_count=0, seed=42, hs=1, negative=0) + model_neg = word2vec.Word2Vec(vector_size=10, min_count=0, seed=42, hs=0, negative=5) model_hs.build_vocab_from_freq(freq_dict) model_neg.build_vocab_from_freq(freq_dict) self.assertEqual(len(model_hs.wv.vocab), 12) @@ -123,7 +123,7 @@ def testPruneVocab(self): ["system", "eps"], ["graph", "system"] ] - model = word2vec.Word2Vec(sentences, size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0) + model = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0) self.assertEqual(len(model.wv.vocab), 2) self.assertEqual(model.wv.vocab['graph'].count, 3) self.assertEqual(model.wv.vocab['system'].count, 4) @@ -135,43 +135,43 @@ def testPruneVocab(self): ["graph", "system"], ["minors", "survey", "minors", "survey", "minors"] ] - model = word2vec.Word2Vec(sentences, size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0) + model = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0) self.assertEqual(len(model.wv.vocab), 3) self.assertEqual(model.wv.vocab['graph'].count, 3) self.assertEqual(model.wv.vocab['minors'].count, 3) self.assertEqual(model.wv.vocab['system'].count, 4) def testTotalWordCount(self): - model = word2vec.Word2Vec(size=10, min_count=0, seed=42) - total_words = model.vocabulary.scan_vocab(sentences)[0] + model = word2vec.Word2Vec(vector_size=10, min_count=0, seed=42) + total_words = model.scan_vocab(sentences)[0] self.assertEqual(total_words, 29) def testMaxFinalVocab(self): # Test for less restricting effect of max_final_vocab # max_final_vocab is specified but has no effect - model = word2vec.Word2Vec(size=10, max_final_vocab=4, min_count=4, sample=0) - model.vocabulary.scan_vocab(sentences) - reported_values = model.vocabulary.prepare_vocab(wv=model.wv, hs=0, negative=0) + model = word2vec.Word2Vec(vector_size=10, max_final_vocab=4, min_count=4, sample=0) + model.scan_vocab(sentences) + reported_values = model.prepare_vocab() self.assertEqual(reported_values['drop_unique'], 11) self.assertEqual(reported_values['retain_total'], 4) self.assertEqual(reported_values['num_retained_words'], 1) - self.assertEqual(model.vocabulary.effective_min_count, 4) + self.assertEqual(model.effective_min_count, 4) # Test for more restricting effect of max_final_vocab # results in setting a min_count more restricting than specified min_count - model = word2vec.Word2Vec(size=10, max_final_vocab=4, min_count=2, sample=0) - model.vocabulary.scan_vocab(sentences) - reported_values = model.vocabulary.prepare_vocab(wv=model.wv, hs=0, negative=0) + model = word2vec.Word2Vec(vector_size=10, max_final_vocab=4, min_count=2, sample=0) + model.scan_vocab(sentences) + reported_values = model.prepare_vocab() self.assertEqual(reported_values['drop_unique'], 8) self.assertEqual(reported_values['retain_total'], 13) self.assertEqual(reported_values['num_retained_words'], 4) - self.assertEqual(model.vocabulary.effective_min_count, 3) + self.assertEqual(model.effective_min_count, 3) def testOnlineLearning(self): """Test that the algorithm is able to add new words to the vocabulary and to a trained model when using a sorted vocabulary""" - model_hs = word2vec.Word2Vec(sentences, size=10, min_count=0, seed=42, hs=1, negative=0) - model_neg = word2vec.Word2Vec(sentences, size=10, min_count=0, seed=42, hs=0, negative=5) + model_hs = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, seed=42, hs=1, negative=0) + model_neg = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, seed=42, hs=0, negative=5) self.assertTrue(len(model_hs.wv.vocab), 12) self.assertTrue(model_hs.wv.vocab['graph'].count, 3) model_hs.build_vocab(new_sentences, update=True) @@ -185,7 +185,7 @@ def testOnlineLearningAfterSave(self): """Test that the algorithm is able to add new words to the vocabulary and to a trained model when using a sorted vocabulary""" tmpf = get_tmpfile('gensim_word2vec.tst') - model_neg = word2vec.Word2Vec(sentences, size=10, min_count=0, seed=42, hs=0, negative=5) + model_neg = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, seed=42, hs=0, negative=5) model_neg.save(tmpf) model_neg = word2vec.Word2Vec.load(tmpf) self.assertTrue(len(model_neg.wv.vocab), 12) @@ -202,8 +202,10 @@ def testOnlineLearningFromFile(self): utils.save_as_line_sentence(sentences, corpus_file) utils.save_as_line_sentence(new_sentences, new_corpus_file) - model_hs = word2vec.Word2Vec(corpus_file=corpus_file, size=10, min_count=0, seed=42, hs=1, negative=0) - model_neg = word2vec.Word2Vec(corpus_file=corpus_file, size=10, min_count=0, seed=42, hs=0, negative=5) + model_hs = word2vec.Word2Vec(corpus_file=corpus_file, vector_size=10, min_count=0, seed=42, + hs=1, negative=0) + model_neg = word2vec.Word2Vec(corpus_file=corpus_file, vector_size=10, min_count=0, seed=42, + hs=0, negative=5) self.assertTrue(len(model_hs.wv.vocab), 12) self.assertTrue(model_hs.wv.vocab['graph'].count, 3) model_hs.build_vocab(corpus_file=new_corpus_file, update=True) @@ -227,7 +229,8 @@ def testOnlineLearningAfterSaveFromFile(self): utils.save_as_line_sentence(new_sentences, new_corpus_file) tmpf = get_tmpfile('gensim_word2vec.tst') - model_neg = word2vec.Word2Vec(corpus_file=corpus_file, size=10, min_count=0, seed=42, hs=0, negative=5) + model_neg = word2vec.Word2Vec(corpus_file=corpus_file, vector_size=10, min_count=0, seed=42, + hs=0, negative=5) model_neg.save(tmpf) model_neg = word2vec.Word2Vec.load(tmpf) self.assertTrue(len(model_neg.wv.vocab), 12) @@ -260,19 +263,19 @@ def onlineSanity(self, model, trained_model=False): def test_sg_hs_online(self): """Test skipgram w/ hierarchical softmax""" - model = word2vec.Word2Vec(sg=1, window=5, hs=1, negative=0, min_count=3, iter=10, seed=42, workers=2) + model = word2vec.Word2Vec(sg=1, window=5, hs=1, negative=0, min_count=3, epochs=10, seed=42, workers=2) self.onlineSanity(model) def test_sg_neg_online(self): """Test skipgram w/ negative sampling""" - model = word2vec.Word2Vec(sg=1, window=4, hs=0, negative=15, min_count=3, iter=10, seed=42, workers=2) + model = word2vec.Word2Vec(sg=1, window=4, hs=0, negative=15, min_count=3, epochs=10, seed=42, workers=2) self.onlineSanity(model) def test_cbow_hs_online(self): """Test CBOW w/ hierarchical softmax""" model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=3, iter=10, seed=42, workers=2 + min_count=3, epochs=10, seed=42, workers=2 ) self.onlineSanity(model) @@ -280,7 +283,7 @@ def test_cbow_neg_online(self): """Test CBOW w/ negative sampling""" model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=15, - min_count=5, iter=10, seed=42, workers=2, sample=0 + min_count=5, epochs=10, seed=42, workers=2, sample=0 ) self.onlineSanity(model) @@ -356,7 +359,7 @@ def testVectorsNormNotSaved(self): loaded_kv = keyedvectors.KeyedVectors.load(tmpf) self.assertTrue(loaded_kv.vectors_norm is None) - def testLoadPreKeyedVectorModel(self): + def obsolete_testLoadPreKeyedVectorModel(self): """Test loading pre-KeyedVectors word2vec model""" if sys.version_info[:2] == (3, 4): @@ -370,13 +373,13 @@ def testLoadPreKeyedVectorModel(self): model_file = 'word2vec_pre_kv%s' % model_file_suffix model = word2vec.Word2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) # Model stored in multiple files model_file = 'word2vec_pre_kv_sep%s' % model_file_suffix model = word2vec.Word2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) def testLoadPreKeyedVectorModelCFormat(self): """Test loading pre-KeyedVectors word2vec model saved in word2vec format""" @@ -479,6 +482,8 @@ def testPersistenceWord2VecFormatCombinationWithStandardPersistence(self): testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) binary_model_with_vocab_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) + print("BIN") + print(binary_model_with_vocab_kv) binary_model_with_vocab_kv.save(tmpf) self.assertRaises(AttributeError, word2vec.Word2Vec.load, tmpf) @@ -524,11 +529,11 @@ def testVocab(self): def testTraining(self): """Test word2vec training.""" # build vocabulary, don't train yet - model = word2vec.Word2Vec(size=2, min_count=1, hs=1, negative=0) + model = word2vec.Word2Vec(vector_size=2, min_count=1, hs=1, negative=0) model.build_vocab(sentences) self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.trainables.syn1.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) @@ -541,7 +546,7 @@ def testTraining(self): self.assertEqual(sims, sims2) # build vocab and train in one step; must be the same as above - model2 = word2vec.Word2Vec(sentences, size=2, min_count=1, hs=1, negative=0) + model2 = word2vec.Word2Vec(sentences, vector_size=2, min_count=1, hs=1, negative=0) self.models_equal(model, model2) @unittest.skipIf(os.name == 'nt' and six.PY2, "CythonLineSentence is not supported on Windows + Py27") @@ -551,11 +556,11 @@ def testTrainingFromFile(self): with temporary_file(get_tmpfile('gensim_word2vec.tst')) as tf: utils.save_as_line_sentence(sentences, tf) - model = word2vec.Word2Vec(size=2, min_count=1, hs=1, negative=0) + model = word2vec.Word2Vec(vector_size=2, min_count=1, hs=1, negative=0) model.build_vocab(corpus_file=tf) self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.trainables.syn1.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) model.train(corpus_file=tf, total_words=model.corpus_total_words, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) @@ -569,7 +574,7 @@ def testTrainingFromFile(self): def testScoring(self): """Test word2vec scoring.""" - model = word2vec.Word2Vec(sentences, size=2, min_count=1, hs=1, negative=0) + model = word2vec.Word2Vec(sentences, vector_size=2, min_count=1, hs=1, negative=0) # just score and make sure they exist scores = model.score(sentences, len(sentences)) @@ -580,14 +585,14 @@ def testLocking(self): corpus = LeeCorpus() # build vocabulary, don't train yet for sg in range(2): # test both cbow and sg - model = word2vec.Word2Vec(size=4, hs=1, negative=5, min_count=1, sg=sg, window=5) + model = word2vec.Word2Vec(vector_size=4, hs=1, negative=5, min_count=1, sg=sg, window=5) model.build_vocab(corpus) # remember two vectors locked0 = np.copy(model.wv.vectors[0]) unlocked1 = np.copy(model.wv.vectors[1]) # lock the vector in slot 0 against change - model.trainables.vectors_lockf[0] = 0.0 + model.wv.vectors_lockf[0] = 0.0 model.train(corpus, total_examples=model.corpus_count, epochs=model.epochs) self.assertFalse((unlocked1 == model.wv.vectors[1]).all()) # unlocked vector should vary @@ -609,7 +614,7 @@ def testEvaluateWordAnalogies(self): def testEvaluateWordPairs(self): """Test Spearman and Pearson correlation coefficients give sane results on similarity datasets""" corpus = word2vec.LineSentence(datapath('head500.noblanks.cor.bz2')) - model = word2vec.Word2Vec(corpus, min_count=3, iter=10) + model = word2vec.Word2Vec(corpus, min_count=3, epochs=10) correlation = model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) pearson = correlation[0][0] spearman = correlation[1][0] @@ -624,7 +629,7 @@ def testEvaluateWordPairsFromFile(self): with temporary_file(get_tmpfile('gensim_word2vec.tst')) as tf: utils.save_as_line_sentence(word2vec.LineSentence(datapath('head500.noblanks.cor.bz2')), tf) - model = word2vec.Word2Vec(corpus_file=tf, min_count=3, iter=10) + model = word2vec.Word2Vec(corpus_file=tf, min_count=3, epochs=10) correlation = model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) pearson = correlation[0][0] spearman = correlation[1][0] @@ -658,29 +663,29 @@ def model_sanity(self, model, train=True, with_corpus_file=False): def test_sg_hs(self): """Test skipgram w/ hierarchical softmax""" - model = word2vec.Word2Vec(sg=1, window=4, hs=1, negative=0, min_count=5, iter=10, workers=2) + model = word2vec.Word2Vec(sg=1, window=4, hs=1, negative=0, min_count=5, epochs=10, workers=2) self.model_sanity(model) @unittest.skipIf(os.name == 'nt' and six.PY2, "CythonLineSentence is not supported on Windows + Py27") def test_sg_hs_fromfile(self): - model = word2vec.Word2Vec(sg=1, window=4, hs=1, negative=0, min_count=5, iter=10, workers=2) + model = word2vec.Word2Vec(sg=1, window=4, hs=1, negative=0, min_count=5, epochs=10, workers=2) self.model_sanity(model, with_corpus_file=True) def test_sg_neg(self): """Test skipgram w/ negative sampling""" - model = word2vec.Word2Vec(sg=1, window=4, hs=0, negative=15, min_count=5, iter=10, workers=2) + model = word2vec.Word2Vec(sg=1, window=4, hs=0, negative=15, min_count=5, epochs=10, workers=2) self.model_sanity(model) @unittest.skipIf(os.name == 'nt' and six.PY2, "CythonLineSentence is not supported on Windows + Py27") def test_sg_neg_fromfile(self): - model = word2vec.Word2Vec(sg=1, window=4, hs=0, negative=15, min_count=5, iter=10, workers=2) + model = word2vec.Word2Vec(sg=1, window=4, hs=0, negative=15, min_count=5, epochs=10, workers=2) self.model_sanity(model, with_corpus_file=True) def test_cbow_hs(self): """Test CBOW w/ hierarchical softmax""" model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=8, hs=1, negative=0, - min_count=5, iter=10, workers=2, batch_words=1000 + min_count=5, epochs=10, workers=2, batch_words=1000 ) self.model_sanity(model) @@ -688,7 +693,7 @@ def test_cbow_hs(self): def test_cbow_hs_fromfile(self): model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=8, hs=1, negative=0, - min_count=5, iter=10, workers=2, batch_words=1000 + min_count=5, epochs=10, workers=2, batch_words=1000 ) self.model_sanity(model, with_corpus_file=True) @@ -696,7 +701,7 @@ def test_cbow_neg(self): """Test CBOW w/ negative sampling""" model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=15, - min_count=5, iter=10, workers=2, sample=0 + min_count=5, epochs=10, workers=2, sample=0 ) self.model_sanity(model) @@ -704,12 +709,12 @@ def test_cbow_neg(self): def test_cbow_neg_fromfile(self): model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=15, - min_count=5, iter=10, workers=2, sample=0 + min_count=5, epochs=10, workers=2, sample=0 ) self.model_sanity(model, with_corpus_file=True) def test_cosmul(self): - model = word2vec.Word2Vec(sentences, size=2, min_count=1, hs=1, negative=0) + model = word2vec.Word2Vec(sentences, vector_size=2, min_count=1, hs=1, negative=0) sims = model.wv.most_similar_cosmul('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar @@ -723,10 +728,10 @@ def testTrainingCbow(self): """Test CBOW word2vec training.""" # to test training, make the corpus larger by repeating its sentences over and over # build vocabulary, don't train yet - model = word2vec.Word2Vec(size=2, min_count=1, sg=0, hs=1, negative=0) + model = word2vec.Word2Vec(vector_size=2, min_count=1, sg=0, hs=1, negative=0) model.build_vocab(sentences) self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.trainables.syn1.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) @@ -739,17 +744,17 @@ def testTrainingCbow(self): self.assertEqual(sims, sims2) # build vocab and train in one step; must be the same as above - model2 = word2vec.Word2Vec(sentences, size=2, min_count=1, sg=0, hs=1, negative=0) + model2 = word2vec.Word2Vec(sentences, vector_size=2, min_count=1, sg=0, hs=1, negative=0) self.models_equal(model, model2) def testTrainingSgNegative(self): """Test skip-gram (negative sampling) word2vec training.""" # to test training, make the corpus larger by repeating its sentences over and over # build vocabulary, don't train yet - model = word2vec.Word2Vec(size=2, min_count=1, sg=1, hs=0, negative=2) + model = word2vec.Word2Vec(vector_size=2, min_count=1, sg=1, hs=0, negative=2) model.build_vocab(sentences) self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) @@ -762,17 +767,17 @@ def testTrainingSgNegative(self): self.assertEqual(sims, sims2) # build vocab and train in one step; must be the same as above - model2 = word2vec.Word2Vec(sentences, size=2, min_count=1, sg=1, hs=0, negative=2) + model2 = word2vec.Word2Vec(sentences, vector_size=2, min_count=1, sg=1, hs=0, negative=2) self.models_equal(model, model2) def testTrainingCbowNegative(self): """Test CBOW (negative sampling) word2vec training.""" # to test training, make the corpus larger by repeating its sentences over and over # build vocabulary, don't train yet - model = word2vec.Word2Vec(size=2, min_count=1, sg=0, hs=0, negative=2) + model = word2vec.Word2Vec(vector_size=2, min_count=1, sg=0, hs=0, negative=2) model.build_vocab(sentences) self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) @@ -785,13 +790,13 @@ def testTrainingCbowNegative(self): self.assertEqual(sims, sims2) # build vocab and train in one step; must be the same as above - model2 = word2vec.Word2Vec(sentences, size=2, min_count=1, sg=0, hs=0, negative=2) + model2 = word2vec.Word2Vec(sentences, vector_size=2, min_count=1, sg=0, hs=0, negative=2) self.models_equal(model, model2) def testSimilarities(self): """Test similarity and n_similarity methods.""" # The model is trained using CBOW - model = word2vec.Word2Vec(size=2, min_count=1, sg=0, hs=0, negative=2) + model = word2vec.Word2Vec(vector_size=2, min_count=1, sg=0, hs=0, negative=2) model.build_vocab(sentences) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) @@ -803,7 +808,7 @@ def testSimilarities(self): def testSimilarBy(self): """Test word2vec similar_by_word and similar_by_vector.""" - model = word2vec.Word2Vec(sentences, size=2, min_count=1, hs=1, negative=0) + model = word2vec.Word2Vec(sentences, vector_size=2, min_count=1, hs=1, negative=0) wordsims = model.wv.similar_by_word('graph', topn=10) wordsims2 = model.wv.most_similar(positive='graph', topn=10) vectorsims = model.wv.similar_by_vector(model.wv['graph'], topn=10) @@ -833,9 +838,9 @@ def models_equal(self, model, model2): self.assertEqual(len(model.wv.vocab), len(model2.wv.vocab)) self.assertTrue(np.allclose(model.wv.vectors, model2.wv.vectors)) if model.hs: - self.assertTrue(np.allclose(model.trainables.syn1, model2.trainables.syn1)) + self.assertTrue(np.allclose(model.syn1, model2.syn1)) if model.negative: - self.assertTrue(np.allclose(model.trainables.syn1neg, model2.trainables.syn1neg)) + self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) most_common_word = max(model.wv.vocab.items(), key=lambda item: item[1].count)[0] self.assertTrue(np.allclose(model.wv[most_common_word], model2.wv[most_common_word])) @@ -871,9 +876,9 @@ def testLoadOldModel(self): self.assertTrue(model.wv.vectors.shape == (12, 100)) self.assertTrue(len(model.wv.vocab) == 12) self.assertTrue(len(model.wv.index2word) == 12) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.wv.vector_size)) - self.assertTrue(model.trainables.vectors_lockf.shape == (12,)) - self.assertTrue(model.vocabulary.cum_table.shape == (12,)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.wv.vector_size)) + self.assertTrue(model.wv.vectors_lockf.shape == (12,)) + self.assertTrue(model.cum_table.shape == (12,)) self.onlineSanity(model, trained_model=True) @@ -886,13 +891,13 @@ def testLoadOldModelSeparates(self): self.assertTrue(model.wv.vectors.shape == (12, 100)) self.assertTrue(len(model.wv.vocab) == 12) self.assertTrue(len(model.wv.index2word) == 12) - self.assertTrue(model.trainables.syn1neg.shape == (len(model.wv.vocab), model.wv.vector_size)) - self.assertTrue(model.trainables.vectors_lockf.shape == (12,)) - self.assertTrue(model.vocabulary.cum_table.shape == (12,)) + self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.wv.vector_size)) + self.assertTrue(model.wv.vectors_lockf.shape == (12,)) + self.assertTrue(model.cum_table.shape == (12,)) self.onlineSanity(model, trained_model=True) - def test_load_old_models_pre_1_0(self): + def obsolete_test_load_old_models_pre_1_0(self): """Test loading pre-1.0 models""" # load really old model model_file = 'w2v-lee-v0.12.0' @@ -934,7 +939,7 @@ def test_load_old_models_3_x(self): model_file = 'word2vec_3.3' model = word2vec.Word2Vec.load(datapath(model_file)) self.assertEqual(model.max_final_vocab, None) - self.assertEqual(model.vocabulary.max_final_vocab, None) + self.assertEqual(model.max_final_vocab, None) old_versions = [ '3.0.0', '3.1.0', '3.2.0', '3.3.0', '3.4.0' @@ -949,7 +954,14 @@ def _check_old_version(self, old_version): model = word2vec.Word2Vec.load(saved_models_dir.format(old_version)) self.assertIsNone(model.corpus_total_words) self.assertTrue(len(model.wv.vocab) == 3) - self.assertTrue(model.wv.vectors.shape == (3, 4)) + try: + self.assertTrue(model.wv.vectors.shape == (3, 4)) + except AttributeError as ae: + print("WV") + print(model.wv) + print(dir(model.wv)) + print(model.wv.syn0) + raise ae # check if similarity search and online training works. self.assertTrue(len(model.wv.most_similar('sentence')) == 2) model.build_vocab(list_corpus, update=True) @@ -989,7 +1001,7 @@ def testTrainWarning(self, line): self.assertTrue(warning in str(line)) def test_train_with_explicit_param(self): - model = word2vec.Word2Vec(size=2, min_count=1, hs=1, negative=0) + model = word2vec.Word2Vec(vector_size=2, min_count=1, hs=1, negative=0) model.build_vocab(sentences) with self.assertRaises(ValueError): model.train(sentences, total_examples=model.corpus_count) From 1d0f52f63398e401df08562fbdc0b75de046a6e3 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Wed, 15 Jan 2020 02:07:17 -0800 Subject: [PATCH 16/60] tune tests for stability, runtimes; rm auto reruns that hide flakiness --- gensim/test/test_doc2vec.py | 26 ++++++++++++++------------ gensim/test/test_ldaseqmodel.py | 3 ++- gensim/test/test_sklearn_api.py | 15 ++++++++++++--- gensim/test/test_word2vec.py | 16 ++++++++-------- tox.ini | 2 +- 5 files changed, 37 insertions(+), 25 deletions(-) diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index 40b5ab2ece..db581916ea 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -390,7 +390,7 @@ def model_sanity(self, model, keep_training=True): """Any non-trivial model on DocsLeeCorpus can pass these sanity checks""" fire1 = 0 # doc 0 sydney fires fire2 = np.int64(8) # doc 8 sydney fires - tennis1 = 6 # doc 6 tennis + alt1 = 29 # doc 29 palestine # inferred vector should be top10 close to bulk-trained one doc0_inferred = model.infer_vector(list(DocsLeeCorpus())[0].words) @@ -418,11 +418,12 @@ def model_sanity(self, model, keep_training=True): for s_id in sims_doc_id: self.assertTrue(len(model.docvecs) // 2 <= s_id <= len(model.docvecs) * 2 // 3) - # tennis doc should be out-of-place among fire news - self.assertEqual(model.docvecs.doesnt_match([fire1, tennis1, fire2]), tennis1) + # fire docs should be closer than fire-alt + self.assertLess(model.docvecs.similarity(fire1, alt1), model.docvecs.similarity(fire1, fire2)) + self.assertLess(model.docvecs.similarity(fire2, alt1), model.docvecs.similarity(fire1, fire2)) - # fire docs should be closer than fire-tennis - self.assertTrue(model.docvecs.similarity(fire1, fire2) > model.docvecs.similarity(fire1, tennis1)) + # alt doc should be out-of-place among fire news + self.assertEqual(model.docvecs.doesnt_match([fire1, alt1, fire2]), alt1) # keep training after save if keep_training: @@ -533,7 +534,7 @@ def test_dmc_hs_fromfile(self): def test_dbow_neg(self): """Test DBOW doc2vec training.""" - model = doc2vec.Doc2Vec(list_corpus, dm=0, hs=0, negative=10, min_count=2, epochs=20) + model = doc2vec.Doc2Vec(list_corpus, vector_size=16, dm=0, hs=0, negative=5, min_count=2, epochs=40) self.model_sanity(model) @unittest.skipIf(os.name == 'nt' and six.PY2, "corpus_file training is not supported on Windows + Py27") @@ -541,7 +542,7 @@ def test_dbow_neg_fromfile(self): """Test DBOW doc2vec training.""" with temporary_file(get_tmpfile('gensim_doc2vec.tst')) as corpus_file: save_lee_corpus_as_line_sentence(corpus_file) - model = doc2vec.Doc2Vec(list_corpus, dm=0, hs=0, negative=10, min_count=2, epochs=20) + model = doc2vec.Doc2Vec(list_corpus, vector_size=16, dm=0, hs=0, negative=5, min_count=2, epochs=40) self.model_sanity(model) def test_dmm_neg(self): @@ -602,12 +603,13 @@ def test_dmc_neg_fromfile(self): self.model_sanity(model) def test_parallel(self): - """Test doc2vec parallel training.""" - corpus = utils.RepeatCorpus(DocsLeeCorpus(), 10000) + """Test doc2vec parallel training with more than default 3 threads.""" + # repeat the ~300 doc (~60000 word) Lee corpus to get 6000 docs (~1.2M words) + corpus = utils.RepeatCorpus(DocsLeeCorpus(), 6000) - for workers in [2, 4]: - model = doc2vec.Doc2Vec(corpus, workers=workers) - self.model_sanity(model) + # use smaller batches-to-workers for more contention + model = doc2vec.Doc2Vec(corpus, workers=6, batch_words=5000) + self.model_sanity(model) def test_deterministic_hs(self): """Test doc2vec results identical with identical RNG seed.""" diff --git a/gensim/test/test_ldaseqmodel.py b/gensim/test/test_ldaseqmodel.py index 227c78b4f6..2982331ceb 100644 --- a/gensim/test/test_ldaseqmodel.py +++ b/gensim/test/test_ldaseqmodel.py @@ -204,7 +204,8 @@ def setUp(self): corpus = [dictionary.doc2bow(text) for text in texts] self.ldaseq = ldaseqmodel.LdaSeqModel( corpus=corpus, id2word=dictionary, num_topics=2, - time_slice=[10, 10, 11], initialize='own', sstats=sstats + time_slice=[10, 10, 11], initialize='own', sstats=sstats, + passes=2, lda_inference_max_iter=10, em_min_iter=1, em_max_iter=4 ) # testing topic word proportions diff --git a/gensim/test/test_sklearn_api.py b/gensim/test/test_sklearn_api.py index f6a8d26c57..9dc7d303eb 100644 --- a/gensim/test/test_sklearn_api.py +++ b/gensim/test/test_sklearn_api.py @@ -1,4 +1,5 @@ import os +import logging import unittest import numpy import codecs @@ -521,7 +522,8 @@ def testModelNotFitted(self): class TestLdaSeqWrapper(unittest.TestCase): def setUp(self): self.model = LdaSeqTransformer( - id2word=dictionary_ldaseq, num_topics=2, time_slice=[10, 10, 11], initialize='gensim' + id2word=dictionary_ldaseq, num_topics=2, time_slice=[10, 10, 11], initialize='gensim', + passes=2, lda_inference_max_iter=10, em_min_iter=1, em_max_iter=4 ) self.model.fit(corpus_ldaseq) @@ -549,7 +551,10 @@ def testPipeline(self): test_target = data.target[0:2] id2word = Dictionary([x.split() for x in test_data]) corpus = [id2word.doc2bow(i.split()) for i in test_data] - model = LdaSeqTransformer(id2word=id2word, num_topics=2, time_slice=[1, 1, 1], initialize='gensim') + model = LdaSeqTransformer( + id2word=id2word, num_topics=2, time_slice=[1, 1, 1], initialize='gensim', + passes=2, lda_inference_max_iter=10, em_min_iter=1, em_max_iter=4 + ) clf = linear_model.LogisticRegression(penalty='l2', C=0.1) text_ldaseq = Pipeline([('features', model,), ('classifier', clf)]) text_ldaseq.fit(corpus, test_target) @@ -582,7 +587,10 @@ def testPersistence(self): self.assertTrue(passed) def testModelNotFitted(self): - ldaseq_wrapper = LdaSeqTransformer(num_topics=2) + ldaseq_wrapper = LdaSeqTransformer( + num_topics=2, + passes=2, lda_inference_max_iter=10, em_min_iter=1, em_max_iter=4 + ) doc = list(corpus_ldaseq)[0] self.assertRaises(NotFittedError, ldaseq_wrapper.transform, doc) @@ -1405,4 +1413,5 @@ def testModelNotFitted(self): if __name__ == '__main__': + logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) unittest.main() diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 28e66bab1c..062df792ca 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -614,14 +614,14 @@ def testEvaluateWordAnalogies(self): def testEvaluateWordPairs(self): """Test Spearman and Pearson correlation coefficients give sane results on similarity datasets""" corpus = word2vec.LineSentence(datapath('head500.noblanks.cor.bz2')) - model = word2vec.Word2Vec(corpus, min_count=3, epochs=10) + model = word2vec.Word2Vec(corpus, min_count=3, epochs=20) correlation = model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) pearson = correlation[0][0] spearman = correlation[1][0] oov = correlation[2] - self.assertTrue(0.1 < pearson < 1.0) - self.assertTrue(0.1 < spearman < 1.0) - self.assertTrue(0.0 <= oov < 90.0) + self.assertTrue(0.1 < pearson < 1.0, "pearson %f not between 0.1 & 1.0" % pearson) + self.assertTrue(0.1 < spearman < 1.0, "spearman %f not between 0.1 and 1.0" % spearman) + self.assertTrue(0.0 <= oov < 90.0, "oov %f not between 0.0 and 90.0" % oov) @unittest.skipIf(os.name == 'nt' and six.PY2, "CythonLineSentence is not supported on Windows + Py27") def testEvaluateWordPairsFromFile(self): @@ -629,14 +629,14 @@ def testEvaluateWordPairsFromFile(self): with temporary_file(get_tmpfile('gensim_word2vec.tst')) as tf: utils.save_as_line_sentence(word2vec.LineSentence(datapath('head500.noblanks.cor.bz2')), tf) - model = word2vec.Word2Vec(corpus_file=tf, min_count=3, epochs=10) + model = word2vec.Word2Vec(corpus_file=tf, min_count=3, epochs=20) correlation = model.wv.evaluate_word_pairs(datapath('wordsim353.tsv')) pearson = correlation[0][0] spearman = correlation[1][0] oov = correlation[2] - self.assertTrue(0.1 < pearson < 1.0) - self.assertTrue(0.1 < spearman < 1.0) - self.assertTrue(0.0 <= oov < 90.0) + self.assertTrue(0.1 < pearson < 1.0, "pearson %f not between 0.1 & 1.0" % pearson) + self.assertTrue(0.1 < spearman < 1.0, "spearman %f not between 0.1 and 1.0" % spearman) + self.assertTrue(0.0 <= oov < 90.0, "oov %f not between 0.0 and 90.0" % oov) def model_sanity(self, model, train=True, with_corpus_file=False): """Even tiny models trained on LeeCorpus should pass these sanity checks""" diff --git a/tox.ini b/tox.ini index e7ca40eaaf..927a8deacc 100644 --- a/tox.ini +++ b/tox.ini @@ -17,7 +17,7 @@ ignore = F821 ; TODO remove me when all examples in docstrings will be executab exclude=.venv, .git, .tox, dist, doc, build, gensim/models/deprecated [pytest] -addopts = -rfxEXs --durations=20 --showlocals --reruns 3 --reruns-delay 1 +addopts = -rfxEXs --durations=20 --showlocals [testenv] recreate = True From 812359605ce4581f2311f4368fe30c73124f981a Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 26 Dec 2019 12:19:11 -0800 Subject: [PATCH 17/60] fix numpy FutureWarning: arrays to stack must be sequence --- gensim/models/keyedvectors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index d7ba89ce8e..87b0fe771a 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -809,7 +809,7 @@ def doesnt_match(self, 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) + 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] From c5efb24ebf97b2cc4d0a03fc88ac283e39432a6f Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 21 Jan 2020 22:58:13 -0800 Subject: [PATCH 18/60] (commented-out) deoptimization option --- setup.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 3f5423e813..d6c55eb728 100644 --- a/setup.py +++ b/setup.py @@ -50,7 +50,14 @@ def make_c_ext(use_cython=False): for module, source in c_extensions.items(): if use_cython: source = source.replace('.c', '.pyx') - yield Extension(module, sources=[source], language='c') + extra_args = [] +# extra_args.extend(['-g', '-O0']) # uncomment if optimization limiting crash info + yield Extension( + module, + sources=[source], + language='c', + extra_compile_args=extra_args, + ) def make_cpp_ext(use_cython=False): @@ -61,7 +68,7 @@ def make_cpp_ext(use_cython=False): extra_args.append('-std=c++11') elif system == 'Darwin': extra_args.extend(['-stdlib=libc++', '-std=c++11']) - +# extra_args.extend(['-g', '-O0']) # uncomment if optimization limiting crash info for module, source in cpp_extensions.items(): if use_cython: source = source.replace('.cpp', '.pyx') From 2c234dd72a289256ee4d7282b2f2d4198c3d9202 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 21 Jan 2020 22:59:33 -0800 Subject: [PATCH 19/60] stronger FB model testing; no _unpack_copy test --- gensim/test/test_fasttext.py | 49 ++++++++++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 11 deletions(-) diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index 26fffa89bb..dbd37a025e 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -195,9 +195,23 @@ def test_norm_vectors_not_saved(self): self.assertTrue(loaded_kv.vectors_norm is None) def model_sanity(self, model): + self.model_structural_sanity(model) + # TODO: add semantic tests, where appropriate + + def model_structural_sanity(self, model): + """Check a model for basic self-consistency, necessary properties & property + correspondences, but no semantic tests.""" self.assertEqual(model.wv.vectors.shape, (len(model.wv.vocab), model.vector_size)) self.assertEqual(model.wv.vectors_vocab.shape, (len(model.wv.vocab), model.vector_size)) self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model.vector_size)) + self.assertEqual(len(model.wv.vectors_ngrams_lockf), len(model.wv.vectors_ngrams)) + self.assertEqual(len(model.wv.vectors_vocab_lockf), len(model.wv.index2key)) + self.assertTrue(np.isfinite(model.wv.vectors_ngrams).all(), "NaN in ngrams") + self.assertTrue(np.isfinite(model.wv.vectors_vocab).all(), "NaN in vectors_vocab") + if model.negative: + self.assertTrue(np.isfinite(model.syn1neg).all(), "NaN in syn1neg") + if model.hs: + self.assertTrue(np.isfinite(model.syn1).all(), "NaN in syn1neg") def test_load_fasttext_format(self): try: @@ -970,6 +984,7 @@ def compare_vocabulary(a, b, t): class NativeTrainingContinuationTest(unittest.TestCase): maxDiff = None + model_structural_sanity = TestFastTextModel.model_structural_sanity def setUp(self): # @@ -999,6 +1014,8 @@ def test_in_vocab(self): actual_vector = native.wv.word_vec(word) self.assertTrue(np.allclose(expected_vector, actual_vector, atol=1e-5)) + self.model_structural_sanity(native) + def test_out_of_vocab(self): """Test for correct representation of out-of-vocab words.""" native = load_native() @@ -1007,6 +1024,8 @@ def test_out_of_vocab(self): actual_vector = native.wv.word_vec(word) self.assertTrue(np.allclose(expected_vector, actual_vector, atol=1e-5)) + self.model_structural_sanity(native) + @unittest.skip('this test does not pass currently, I suspect a bug in our FT implementation') def test_out_of_vocab_gensim(self): """Test whether gensim gives similar results to FB for OOV words. @@ -1019,6 +1038,8 @@ def test_out_of_vocab_gensim(self): actual_vector = model.wv.word_vec(word) self.assertTrue(np.allclose(expected_vector, actual_vector, atol=1e-5)) + self.model_structural_sanity(model) + def test_sanity(self): """Compare models trained on toy data. They should be equal.""" trained = train_gensim() @@ -1034,25 +1055,31 @@ def test_sanity(self): compare_vocabulary(trained, native, self) compare_nn(trained, native, self) + self.model_structural_sanity(trained) + self.model_structural_sanity(native) + def test_continuation_native(self): """Ensure that training has had a measurable effect.""" native = load_native() + self.model_structural_sanity(native) # # Pick a word that's is in both corpuses. # Its vectors should be different between training runs. # - word = 'human' + word = 'human' # FIXME: this isn't actually in model, except via OOV ngrams old_vector = native.wv.word_vec(word).tolist() native.train(list_corpus, total_examples=len(list_corpus), epochs=native.epochs) new_vector = native.wv.word_vec(word).tolist() self.assertNotEqual(old_vector, new_vector) + self.model_structural_sanity(native) def test_continuation_gensim(self): """Ensure that continued training has had a measurable effect.""" model = train_gensim(min_count=0) + self.model_structural_sanity(model) vectors_ngrams_before = np.copy(model.wv.vectors_ngrams) word = 'human' @@ -1065,12 +1092,15 @@ def test_continuation_gensim(self): new_vector = model.wv.word_vec(word).tolist() self.assertNotEqual(old_vector, new_vector) + self.model_structural_sanity(model) def test_continuation_load_gensim(self): # # This is a model from 3.6.0 # model = FT_gensim.load(datapath('compatible-hash-false.model')) + self.model_structural_sanity(model) + vectors_ngrams_before = np.copy(model.wv.vectors_ngrams) old_vector = model.wv.word_vec('human').tolist() @@ -1079,6 +1109,7 @@ def test_continuation_load_gensim(self): self.assertFalse(np.allclose(vectors_ngrams_before, model.wv.vectors_ngrams)) self.assertNotEqual(old_vector, new_vector) + self.model_structural_sanity(model) def test_save_load_gensim(self): """Test that serialization works end-to-end. Not crashing is a success.""" @@ -1094,9 +1125,11 @@ def test_save_load_gensim(self): train_gensim().save(model_name) model = FT_gensim.load(model_name) + self.model_structural_sanity(model) model.train(list_corpus, total_examples=len(list_corpus), epochs=model.epochs) model.save(model_name) + self.model_structural_sanity(model) def test_save_load_native(self): """Test that serialization works end-to-end. Not crashing is a success.""" @@ -1107,15 +1140,18 @@ def test_save_load_native(self): load_native().save(model_name) model = FT_gensim.load(model_name) + self.model_structural_sanity(model) model.train(list_corpus, total_examples=len(list_corpus), epochs=model.epochs) model.save(model_name) + self.model_structural_sanity(model) def test_load_native_pretrained(self): model = gensim.models.fasttext.load_facebook_model(datapath('toy-model-pretrained.bin')) actual = model.wv['monarchist'] expected = np.array([0.76222, 1.0669, 0.7055, -0.090969, -0.53508]) self.assertTrue(np.allclose(expected, actual, atol=10e-4)) + self.model_structural_sanity(model) def test_load_native_vectors(self): cap_path = datapath("crime-and-punishment.bin") @@ -1132,6 +1168,7 @@ def test_no_ngrams(self): v1 = model.wv[''] origin = np.zeros(v1.shape, v1.dtype) self.assertTrue(np.allclose(v1, origin)) + self.model_structural_sanity(model) def _train_model_with_pretrained_vectors(): @@ -1772,16 +1809,6 @@ def test_ft_kv_backward_compat_w_360(self): class UnpackTest(unittest.TestCase): - def test_copy_sanity(self): - m = np.array(range(9)) - m.shape = (3, 3) - hash2index = {10: 0, 11: 1, 12: 2} - - n = _unpack_copy(m, 25, hash2index) - self.assertTrue(np.all(m[0] == n[10])) - self.assertTrue(np.all(m[1] == n[11])) - self.assertTrue(np.all(m[2] == n[12])) - def test_sanity(self): m = np.array(range(9)) m.shape = (3, 3) From 9910404de5d2b6e61a48a9434ba23f673de87058 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 21 Jan 2020 23:05:45 -0800 Subject: [PATCH 20/60] merge redundant methods; rm duplicated imports/defs --- gensim/models/fasttext.py | 10 +-- gensim/models/fasttext_inner.pyx | 114 ++++--------------------------- 2 files changed, 15 insertions(+), 109 deletions(-) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index bdc2cf9319..81257d31a5 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -294,9 +294,7 @@ try: from gensim.models.fasttext_inner import ( # noqa: F401 - train_batch_sg, - train_batch_cbow, - FAST_VERSION, + train_batch_any, MAX_WORDS_IN_BATCH, compute_ngrams, compute_ngrams_bytes, @@ -680,11 +678,7 @@ def _do_train_job(self, sentences, alpha, inits): """ work, neu1 = inits - tally = 0 - if self.sg: - tally += train_batch_sg(self, sentences, alpha, work, neu1) - else: - tally += train_batch_cbow(self, sentences, alpha, work, neu1) + tally = train_batch_any(self, sentences, alpha, work, neu1) return tally, self._raw_word_count(sentences) diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index 0702729c90..dcf1f80055 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -7,16 +7,16 @@ """Optimized Cython functions for training a :class:`~gensim.models.fasttext.FastText` model. -The main entry points are :func:`~gensim.models.fasttext_inner.train_batch_sg` -and :func:`~gensim.models.fasttext_inner.train_batch_cbow`. They may be -called directly from Python code. +The main entry point is :func:`~gensim.models.fasttext_inner.train_batch_any` +which may be called directly from Python code. Notes ----- The implementation of the above functions heavily depends on the FastTextConfig struct defined in :file:`gensim/models/fasttext_inner.pxd`. -The FAST_VERSION constant determines what flavor of BLAS we're currently using: +The gensim.models.word2vec.FAST_VERSION value reports what flavor of BLAS +we're currently using: 0: double 1: float @@ -36,12 +36,6 @@ from libc.math cimport exp from libc.math cimport log from libc.string cimport memset -# scipy <= 0.15 -try: - from scipy.linalg.blas import fblas -except ImportError: - # in scipy > 0.15, fblas function has been removed - import scipy.linalg.blas as fblas # # We make use of the following BLAS functions (or their analogs, if BLAS is @@ -59,14 +53,10 @@ except ImportError: # # The increments (inc_x and inc_y) are usually 1 in our case. # +# The versions are as chosen in word2vec_inner.pyx, and aliased to `our_` functions -# -# FIXME: why are we importing EXP_TABLE and then redefining it? -# -from word2vec_inner cimport bisect_left, random_int32, scopy, saxpy, dsdot, sscal, \ - REAL_t, EXP_TABLE, our_dot, our_saxpy, our_dot_double, our_dot_float, our_dot_noblas, our_saxpy_noblas - -REAL = np.float32 +from word2vec_inner cimport bisect_left, random_int32, scopy, sscal, \ + REAL_t, our_dot, our_saxpy DEF MAX_SENTENCE_LEN = 10000 DEF MAX_SUBWORDS = 1000 @@ -606,10 +596,7 @@ cdef void fasttext_train_any(FastTextConfig *c, int num_sentences, int sg) nogil else: for j in range(window_start, window_end): if j == i: - # - # TODO: why do we ignore the token at the "center" of - # the window? - # + # no reason to train a center word as predicting itself continue if c.hs: fasttext_fast_sentence_sg_hs(c, i, j) @@ -617,51 +604,7 @@ cdef void fasttext_train_any(FastTextConfig *c, int num_sentences, int sg) nogil fasttext_fast_sentence_sg_neg(c, i, j) -def train_batch_sg(model, sentences, alpha, _work, _l1): - """Update skip-gram model by training on a sequence of sentences. - - Each sentence is a list of string tokens, which are looked up in the model's - vocab dictionary. Called internally from :meth:`~gensim.models.fasttext.FastText.train`. - - Parameters - ---------- - model : :class:`~gensim.models.fasttext.FastText` - Model to be trained. - sentences : iterable of list of str - A single batch: part of the corpus streamed directly from disk/network. - alpha : float - Learning rate. - _work : np.ndarray - Private working memory for each worker. - _l1 : np.ndarray - Private working memory for each worker. - - Returns - ------- - int - Effective number of words trained. - - """ - cdef: - FastTextConfig c - int num_words = 0 - int num_sentences = 0 - - init_ft_config(&c, model, alpha, _work, _l1) - - num_words, num_sentences = populate_ft_config(&c, model.wv.vocab, model.wv.buckets_word, sentences) - - # precompute "reduced window" offsets in a single randint() call - for i, randint in enumerate(model.random.randint(0, c.window, num_words)): - c.reduced_windows[i] = randint - - with nogil: - fasttext_train_any(&c, num_sentences, 1) - - return num_words - - -def train_batch_cbow(model, sentences, alpha, _work, _neu1): +def train_batch_any(model, sentences, alpha, _work, _neu1): """Update the CBOW model by training on a sequence of sentences. Each sentence is a list of string tokens, which are looked up in the model's @@ -838,24 +781,10 @@ def init(): """Precompute function `sigmoid(x) = 1 / (1 + exp(-x))`, for x values discretized into table EXP_TABLE. Also calculate log(sigmoid(x)) into LOG_TABLE. - Returns - ------- - {0, 1, 2} - Enumeration to signify underlying data type returned by the BLAS dot product calculation. - 0 signifies double, 1 signifies double, and 2 signifies that custom cython loops were used - instead of BLAS. - + We recalc, rather than re-use the table from word2vec_inner, because Facebook's FastText + code uses a 512-slot table rather than the 1000 precedent of word2vec.c. """ - global our_dot - global our_saxpy - cdef int i - cdef float *x = [10.0] - cdef float *y = [0.01] - cdef float expected = 0.1 - cdef int size = 1 - cdef double d_res - cdef float *p_res # build the sigmoid table for i in range(EXP_TABLE_SIZE): @@ -863,23 +792,6 @@ def init(): EXP_TABLE[i] = (EXP_TABLE[i] / (EXP_TABLE[i] + 1)) LOG_TABLE[i] = log( EXP_TABLE[i] ) - # check whether sdot returns double or float - d_res = dsdot(&size, x, &ONE, y, &ONE) - p_res = &d_res - if abs(d_res - expected) < 0.0001: - our_dot = our_dot_double - our_saxpy = saxpy - return 0 # double - elif abs(p_res[0] - expected) < 0.0001: - our_dot = our_dot_float - our_saxpy = saxpy - return 1 # float - else: - # neither => use cython loops, no BLAS - # actually, the BLAS is so messed up we'll probably have segfaulted above and never even reach here - our_dot = our_dot_noblas - our_saxpy = our_saxpy_noblas - return 2 - -FAST_VERSION = init() # initialize the module + +init() # initialize the module MAX_WORDS_IN_BATCH = MAX_SENTENCE_LEN From 658813f5764cc7ae487eb239bb9e251b88e4826a Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 21 Jan 2020 23:13:19 -0800 Subject: [PATCH 21/60] rationalize _lockf, buckets_word behaviors --- gensim/models/fasttext.py | 147 ++++++++++--------------------- gensim/models/fasttext_inner.pyx | 17 ++-- 2 files changed, 58 insertions(+), 106 deletions(-) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 81257d31a5..f0cb4fb403 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -504,7 +504,7 @@ def init_post_load(self, hidden_output): assert vocab_size > 0, 'expected vocab_size to be initialized already' self.wv.vectors_ngrams_lockf = ones(len(self.wv.vectors_ngrams), dtype=REAL) - self.wv.vectors_vocab_lockf = ones(len(self.wv.vectors_vocab.shape), dtype=REAL) + self.wv.vectors_vocab_lockf = ones(len(self.wv.vectors_vocab), dtype=REAL) if self.hs: self.syn1 = hidden_output @@ -629,7 +629,7 @@ def estimate_memory(self, vocab_size=None, report=None): report['syn0_ngrams'] = num_buckets * vec_size # A tuple (48 bytes) with num_ngrams_word ints (8 bytes) for each word # Only used during training, not stored with the model - report['buckets_word'] = 48 * len(self.wv.vocab) + 8 * num_ngrams + report['buckets_word'] = 48 * len(self.wv.vocab) + 8 * num_ngrams # FIXME: this looks confused -gojomo elif self.word_ngrams > 0: logger.warn( 'subword information is enabled, but no vocabulary could be found, estimated required memory might be ' @@ -761,11 +761,6 @@ def train(self, corpus_iterable=None, corpus_file=None, total_examples=None, tot if corpus_iterable is not None and not isinstance(corpus_iterable, Iterable): raise TypeError("sentences must be an iterable of list, got %r instead" % corpus_iterable) - if self.wv.buckets_word is None: - logger.warn("self.wv.buckets_word was None; fixing.") - self.old_vocab_len = len(self.wv.vocab) - self.wv.init_ngrams_weights(seed=self.seed) - super(FastText, self).train( corpus_iterable=corpus_iterable, corpus_file=corpus_file, total_examples=total_examples, total_words=total_words, @@ -886,6 +881,8 @@ def load(cls, *args, **kwargs): model.bucket = model.wv.bucket _try_upgrade(model.wv) + if not hasattr(model.wv, 'buckets_word') or not model.wv.buckets_word: + model.wv.recalc_word_ngram_buckets() return model @@ -1209,6 +1206,11 @@ def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): buckets_word : dict Maps vocabulary items (by their index) to the buckets they occur in. + When used in training, FastTextKeyedVectors may be decorated with + extra attributes that closely associate with its core attributes, + such as the experimental vectors_vocab_lockf and vectors_ngrams_lockf + training-update-dampening factors. + """ super(FastTextKeyedVectors, self).__init__(vector_size=vector_size) self.vectors_vocab = None # fka syn0_vocab @@ -1346,13 +1348,7 @@ def init_ngrams_weights(self, seed): Call this **after** the vocabulary has been fully initialized. """ - self.buckets_word = _process_fasttext_vocab( - self.vocab.items(), - self.min_n, - self.max_n, - self.bucket, - self.compatible_hash, - ) + self.recalc_word_ngram_buckets() rand_obj = np.random rand_obj.seed(seed) @@ -1388,13 +1384,7 @@ def update_ngrams_weights(self, seed, old_vocab_len): Call this **after** the vocabulary has been updated. """ - self.buckets_word = _process_fasttext_vocab( - self.vocab.items(), - self.min_n, - self.max_n, - self.bucket, - self.compatible_hash, - ) + self.recalc_word_ngram_buckets() rand_obj = np.random rand_obj.seed(seed) @@ -1429,7 +1419,7 @@ def init_post_load(self, fb_vectors): # self.vectors_vocab = np.array(fb_vectors[:vocab_words, :]) self.vectors_ngrams = np.array(fb_vectors[vocab_words:, :]) - self.buckets_word = None # This can get initialized later + self.recalc_word_ngram_buckets() self.adjust_vectors() # calculate composite full-word vectors def adjust_vectors(self): @@ -1445,52 +1435,31 @@ def adjust_vectors(self): self.vectors = self.vectors_vocab[:].copy() for i, w in enumerate(self.index2key): - ngram_hashes = ft_ngram_hashes(w, self.min_n, self.max_n, self.bucket, self.compatible_hash) - for nh in ngram_hashes: + ngram_buckets = self.buckets_word[i] + for nh in ngram_buckets: self.vectors[i] += self.vectors_ngrams[nh] - self.vectors[i] /= len(ngram_hashes) + 1 + self.vectors[i] /= len(ngram_buckets) + 1 -def _process_fasttext_vocab(iterable, min_n, max_n, num_buckets, compatible_hash): - """ - Performs a common operation for FastText weight initialization and - updates: scan the vocabulary, calculate ngrams and their hashes, keep - track of new ngrams, the buckets that each word relates to via its - ngrams, etc. - - Parameters - ---------- - iterable : list - A list of (word, :class:`Vocab`) tuples. - min_n : int - The minimum length of ngrams. - max_n : int - The maximum length of ngrams. - num_buckets : int - The number of buckets used by the model. - compatible_hash : boolean - True for compatibility with the Facebook implementation. - False for compatibility with the old Gensim implementation. - - Returns - ------- - dict - Keys are indices of entities in the vocabulary (words). Values are - arrays containing indices into vectors_ngrams for each ngram of the - word. - - """ - word_indices = {} + def recalc_word_ngram_buckets(self): + """ + Performs a common operation for FastText weight initialization and + updates: scan the vocabulary, calculate ngrams and their hashes, keep + track of new ngrams, the buckets that each word relates to via its + ngrams, etc. - if num_buckets == 0: - return {v.index: np.array([], dtype=np.uint32) for w, v in iterable} + TODO: evaluate if this is even necessary, compared to just recalculating + """ + if self.bucket == 0: + self.buckets_word = [np.array([], dtype=np.uint32)] * len(self.index2key) + return - for word, vocab in iterable: - wi = [] - for ngram_hash in ft_ngram_hashes(word, min_n, max_n, num_buckets, compatible_hash): - wi.append(ngram_hash) - word_indices[vocab.index] = np.array(wi, dtype=np.uint32) + self.buckets_word = [None] * len(self.index2key) - return word_indices + for i, word in enumerate(self.index2key): + self.buckets_word[i] = np.array( + ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash), + dtype=np.uint32, + ) def _pad_random(m, new_rows, rand): @@ -1518,6 +1487,8 @@ def _rollback_optimization(kv): assert hasattr(kv, 'bucket') kv.vectors_ngrams = _unpack(kv.vectors_ngrams, kv.bucket, kv.hash2index) + if hasattr(kv, 'vectors_ngrams_lockf'): + kv.vectors_ngrams_lockf = _unpack(kv.vectors_ngrams_lockf, kv.bucket, kv.hash2index, fill=1.0) # # We have replaced num_ngram_vectors with a property and deprecated it. @@ -1526,33 +1497,7 @@ def _rollback_optimization(kv): del kv.hash2index -def _unpack_copy(m, num_rows, hash2index, seed=1): - """Same as _unpack, but makes a copy of the matrix. - - Simpler implementation, but uses more RAM. - - """ - rows, columns = m.shape - if rows == num_rows: - # - # Nothing to do. - # - return m - assert num_rows > rows - - rand_obj = np.random - rand_obj.seed(seed) - - n = np.empty((0, columns), dtype=m.dtype) - n = _pad_random(n, num_rows, rand_obj) - - for src, dst in hash2index.items(): - n[src] = m[dst] - - return n - - -def _unpack(m, num_rows, hash2index, seed=1): +def _unpack(m, num_rows, hash2index, seed=1, fill=None): """Restore the array to its natural shape, undoing the optimization. A packed matrix contains contiguous vectors for ngrams, as well as a hashmap. @@ -1576,7 +1521,8 @@ def _unpack(m, num_rows, hash2index, seed=1): the product of the optimization we are undoing. seed : float, optional The seed for the PRNG. Will be used to initialize new rows. - + fill : float or array or None, optional + Value for new rows. If None (the default), randomly initialize. Returns ------- np.array @@ -1589,7 +1535,7 @@ def _unpack(m, num_rows, hash2index, seed=1): Throw away the old matrix after calling this function, or use np.copy. """ - orig_rows, orig_columns = m.shape + orig_rows, *more_dims = m.shape if orig_rows == num_rows: # # Nothing to do. @@ -1597,14 +1543,17 @@ def _unpack(m, num_rows, hash2index, seed=1): return m assert num_rows > orig_rows - rand_obj = np.random - rand_obj.seed(seed) + if fill is None: + rand_obj = np.random + rand_obj.seed(seed) - # - # Rows at the top of the matrix (the first orig_rows) will contain "packed" learned vectors. - # Rows at the bottom of the matrix will be "free": initialized to random values. - # - m = _pad_random(m, num_rows - orig_rows, rand_obj) + # + # Rows at the top of the matrix (the first orig_rows) will contain "packed" learned vectors. + # Rows at the bottom of the matrix will be "free": initialized to random values. + # + m = _pad_random(m, num_rows - orig_rows, rand_obj) + else: + m = np.concatenate([m, [fill] * (num_rows - orig_rows)]) # # Swap rows to transform hash2index into the identify function. diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index dcf1f80055..ebaa664438 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -61,8 +61,8 @@ from word2vec_inner cimport bisect_left, random_int32, scopy, sscal, \ DEF MAX_SENTENCE_LEN = 10000 DEF MAX_SUBWORDS = 1000 -DEF EXP_TABLE_SIZE = 1000 -DEF MAX_EXP = 6 +DEF EXP_TABLE_SIZE = 512 +DEF MAX_EXP = 8 cdef REAL_t[EXP_TABLE_SIZE] EXP_TABLE cdef REAL_t[EXP_TABLE_SIZE] LOG_TABLE @@ -325,9 +325,12 @@ cdef void fasttext_fast_sentence_cbow_neg(FastTextConfig *c, int i, int j, int k row2 = target_index * size f_dot = our_dot(&size, neu1, &ONE, &syn1neg[row2], &ONE) - if f_dot <= -MAX_EXP or f_dot >= MAX_EXP: - continue - f = EXP_TABLE[((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))] + if f_dot <= -MAX_EXP: + f = 0.0 + elif f_dot >= MAX_EXP: + f = 1.0 + else: + f = EXP_TABLE[((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))] g = (label - f) * alpha our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE) @@ -486,8 +489,8 @@ cdef object populate_ft_config(FastTextConfig *c, vocab, buckets_word, sentences A pointer to the struct that will contain the populated indices. vocab : dict The vocabulary - buckets_word : dict - A map containing the buckets each word appears in + buckets_word : list + A list containing the buckets each word appears in sentences : iterable The sentences to read From 3cdb1d6e9dcf1a396de8d4c2871c9ede7108a196 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 23 Jan 2020 19:18:17 -0800 Subject: [PATCH 22/60] rename .docvecs to .dv --- gensim/models/doc2vec.py | 77 +++++++++++++++++++++++----------------- 1 file changed, 44 insertions(+), 33 deletions(-) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 4a2a1761ac..cc3be86da7 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -79,6 +79,7 @@ import numpy as np from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc +from gensim.utils import deprecated from gensim.models import Word2Vec from six.moves import range from six import string_types, integer_types, itervalues @@ -277,7 +278,7 @@ def __init__(self, documents=None, corpus_file=None, vector_size=100, dm_mean=No .. sourcecode:: pycon - >>> model.docvecs['doc003'] + >>> model.dv['doc003'] """ corpus_iterable = documents @@ -292,7 +293,7 @@ def __init__(self, documents=None, corpus_file=None, vector_size=100, dm_mean=No logger.info("using concatenative %d-dimensional layer1", self.layer1_size) self.vector_size = vector_size - self.docvecs = docvecs or KeyedVectors(self.vector_size, mapfile_path=docvecs_mapfile) + self.dv = docvecs or KeyedVectors(self.vector_size, mapfile_path=docvecs_mapfile) super(Doc2Vec, self).__init__( sentences=corpus_iterable, @@ -321,6 +322,16 @@ def dbow(self): """ return self.sg # same as SG + @property + @deprecated("The `docvecs` property has been renamed `dv`.") + def docvecs(self): + return self.dv + + @docvecs.setter + @deprecated("The `docvecs` property has been renamed `dv`.") + def docvecs(self, value): + self.dv = value + def _clear_post_train(self): """Alias for :meth:`~gensim.models.doc2vec.Doc2Vec.clear_sims`.""" self.clear_sims() @@ -328,19 +339,19 @@ def _clear_post_train(self): def clear_sims(self): """Resets the current word vectors. """ self.wv.vectors_norm = None - self.docvecs.vectors_norm = None + self.dv.vectors_norm = None def reset_weights(self): super(Doc2Vec, self).reset_weights() - self.docvecs.resize_vectors() - self.docvecs.randomly_initialize_vectors() - if self.docvecs.mapfile_path: - self.docvecs.vectors_lockf = np_memmap( - self.docvecs.mapfile_path + '.vectors_lockf', dtype=REAL, mode='w+', shape=(len(self.docvecs.vectors),) + self.dv.resize_vectors() + self.dv.randomly_initialize_vectors() + if self.dv.mapfile_path: + self.dv.vectors_lockf = np_memmap( + self.dv.mapfile_path + '.vectors_lockf', dtype=REAL, mode='w+', shape=(len(self.dv.vectors),) ) - self.docvecs.vectors_lockf.fill(1.0) + self.dv.vectors_lockf.fill(1.0) else: - self.docvecs.vectors_lockf = ones((len(self.docvecs.vectors),), dtype=REAL) # zeros suppress learning + self.dv.vectors_lockf = ones((len(self.dv.vectors),), dtype=REAL) # zeros suppress learning def reset_from(self, other_model): """Copy shareable data structures from another (possibly pre-trained) model. @@ -355,15 +366,15 @@ def reset_from(self, other_model): self.wv.index2key = other_model.wv.index2key self.cum_table = other_model.cum_table self.corpus_count = other_model.corpus_count - self.docvecs.vocab = other_model.docvecs.vocab - self.docvecs.index2key = other_model.docvecs.index2key + self.dv.vocab = other_model.dv.vocab + self.dv.index2key = other_model.dv.index2key self.reset_weights() def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, total_examples=None, total_words=None, offsets=None, start_doctags=None, **kwargs): work, neu1 = thread_private_mem - doctag_vectors = self.docvecs.vectors - doctag_locks = self.docvecs.vectors_lockf + doctag_vectors = self.dv.vectors + doctag_locks = self.dv.vectors_lockf offset = offsets[thread_id] start_doctag = start_doctags[thread_id] @@ -371,17 +382,17 @@ def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_p if self.sg: examples, tally, raw_tally = d2v_train_epoch_dbow( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, - total_examples, total_words, work, neu1, len(self.docvecs), + total_examples, total_words, work, neu1, len(self.dv), doctag_vectors=doctag_vectors, doctag_locks=doctag_locks, train_words=self.dbow_words) elif self.dm_concat: examples, tally, raw_tally = d2v_train_epoch_dm_concat( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, - total_examples, total_words, work, neu1, len(self.docvecs), + total_examples, total_words, work, neu1, len(self.dv), doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) else: examples, tally, raw_tally = d2v_train_epoch_dm( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, - total_examples, total_words, work, neu1, len(self.docvecs), + total_examples, total_words, work, neu1, len(self.dv), doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) return examples, tally, raw_tally @@ -407,9 +418,9 @@ def _do_train_job(self, job, alpha, inits): work, neu1 = inits tally = 0 for doc in job: - doctag_indexes = [self.docvecs.get_index(tag) for tag in doc.tags if tag in self.docvecs] - doctag_vectors = self.docvecs.vectors - doctag_locks = self.docvecs.vectors_lockf + doctag_indexes = [self.dv.get_index(tag) for tag in doc.tags if tag in self.dv] + doctag_vectors = self.dv.vectors + doctag_locks = self.dv.vectors_lockf if self.sg: tally += train_document_dbow( self, doc.words, doctag_indexes, alpha, work, train_words=self.dbow_words, @@ -576,7 +587,7 @@ def estimated_lookup_memory(self): The estimated RAM required to look up a tag in bytes. """ - return 60 * len(self.docvecs.vocab) + 140 * len(self.docvecs.vocab) + return 60 * len(self.dv.vocab) + 140 * len(self.dv.vocab) def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps=None): """Infer a vector for given post-bulk training document. @@ -613,8 +624,8 @@ def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps min_alpha = min_alpha or self.min_alpha epochs = epochs or self.epochs - doctag_vectors = pseudorandom_weak_vector(self.docvecs.vector_size, seed_string=' '.join(doc_words)) - doctag_vectors = doctag_vectors.reshape(1, self.docvecs.vector_size) + doctag_vectors = pseudorandom_weak_vector(self.dv.vector_size, seed_string=' '.join(doc_words)) + doctag_vectors = doctag_vectors.reshape(1, self.dv.vector_size) doctag_locks = np.ones(1, dtype=REAL) doctag_indexes = [0] @@ -660,7 +671,7 @@ def __getitem__(self, tag): """ if isinstance(tag, string_types + integer_types + (integer,)): if tag not in self.wv.vocab: - return self.docvecs[tag] + return self.dv[tag] return self.wv[tag] return vstack([self[i] for i in tag]) @@ -690,7 +701,7 @@ def __str__(self): segments.append('dm/m') else: segments.append('dm/s') - segments.append('d%d' % self.docvecs.vector_size) # dimensions + segments.append('d%d' % self.dv.vector_size) # dimensions if self.negative: segments.append('n%d' % self.negative) # negative samples if self.hs: @@ -729,7 +740,7 @@ def save_word2vec_format(self, fname, doctag_vec=False, word_vec=True, prefix='* # save word vectors if word_vec: if doctag_vec: - total_vec = len(self.wv) + len(self.docvecs) + total_vec = len(self.wv) + len(self.dv) self.wv.save_word2vec_format(fname, fvocab, binary, total_vec) # save document vectors if doctag_vec: @@ -739,7 +750,7 @@ def save_word2vec_format(self, fname, doctag_vec=False, word_vec=True, prefix='* # simply appending to existing file write_first_line = False append = True - self.docvecs.save_word2vec_format( + self.dv.save_word2vec_format( fname, prefix=prefix, fvocab=fvocab, binary=binary, write_first_line=write_first_line, append=append) @@ -753,7 +764,7 @@ def init_sims(self, replace=False): continue training if call it with `replace=True`). """ - self.docvecs.init_sims(replace=replace) + self.dv.init_sims(replace=replace) @classmethod def load(cls, *args, **kwargs): @@ -809,7 +820,7 @@ def estimate_memory(self, vocab_size=None, report=None): """ report = report or {} report['doctag_lookup'] = self.estimated_lookup_memory() - report['doctag_syn0'] = len(self.docvecs) * self.vector_size * dtype(REAL).itemsize + report['doctag_syn0'] = len(self.dv) * self.vector_size * dtype(REAL).itemsize return super(Doc2Vec, self).estimate_memory(vocab_size, report=report) def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, progress_per=10000, @@ -852,7 +863,7 @@ def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, prog """ total_words, corpus_count = self.scan_vocab( - corpus_iterable=corpus_iterable, corpus_file=corpus_file, docvecs=self.docvecs, + corpus_iterable=corpus_iterable, corpus_file=corpus_file, docvecs=self.dv, progress_per=progress_per, trim_rule=trim_rule ) self.corpus_count = corpus_count @@ -976,8 +987,8 @@ def _scan_vocab(self, corpus_iterable, progress_per, trim_rule): doctags_lookup[key].index = doctags_lookup[key].index + max_rawint + 1 doctags_list = ConcatList([range(0, max_rawint + 1), doctags_list]) - self.docvecs.map = doctags_lookup - self.docvecs.index2key = doctags_list + self.dv.map = doctags_lookup + self.dv.index2key = doctags_list self.raw_vocab = vocab return total_words, corpus_count @@ -1024,7 +1035,7 @@ def scan_vocab(self, corpus_iterable=None, corpus_file=None, docvecs=None, progr logger.info( "collected %i word types and %i unique tags from a corpus of %i examples and %i words", - len(self.raw_vocab), len(self.docvecs), corpus_count, total_words + len(self.raw_vocab), len(self.dv), corpus_count, total_words ) return total_words, corpus_count From 10d9f55574566246506b643b8e689b24b051ffd3 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 27 Jan 2020 19:38:29 -0800 Subject: [PATCH 23/60] update usages; rm obsolete tests; restore gensim.utils import --- gensim/models/_fasttext_bin.py | 10 +-- gensim/models/doc2vec.py | 12 ++-- gensim/models/fasttext.py | 10 +-- gensim/models/translation_matrix.py | 8 +-- gensim/similarities/nmslib.py | 2 +- gensim/sklearn_api/d2vmodel.py | 17 +++--- gensim/test/test_doc2vec.py | 84 +++++++++++++------------- gensim/test/test_fasttext.py | 61 +------------------ gensim/test/test_keyedvectors.py | 3 +- gensim/test/test_similarities.py | 16 ++--- gensim/test/test_translation_matrix.py | 2 +- 11 files changed, 84 insertions(+), 141 deletions(-) diff --git a/gensim/models/_fasttext_bin.py b/gensim/models/_fasttext_bin.py index 3b7af85f9e..7853a7a8d6 100644 --- a/gensim/models/_fasttext_bin.py +++ b/gensim/models/_fasttext_bin.py @@ -435,7 +435,7 @@ def _get_field_from_model(model, field): requested field name, fields are listed in the `_NEW_HEADER_FORMAT` list """ if field == 'bucket': - return model.trainables.bucket + return model.bucket elif field == 'dim': return model.vector_size elif field == 'epoch': @@ -457,7 +457,7 @@ def _get_field_from_model(model, field): elif field == 'minn': return model.wv.min_n elif field == 'min_count': - return model.vocabulary.min_count + return model.min_count elif field == 'model': # `model` => cbow:1, sg:2, sup:3 # cbow = continous bag of words (default) @@ -467,7 +467,7 @@ def _get_field_from_model(model, field): elif field == 'neg': return model.negative elif field == 't': - return model.vocabulary.sample + return model.sample elif field == 'word_ngrams': # This is skipped in gensim loading setting, using the default from FB C++ code return 1 @@ -596,9 +596,9 @@ def _output_save(fout, model): saved model """ if model.hs: - hidden_output = model.trainables.syn1 + hidden_output = model.syn1 if model.negative: - hidden_output = model.trainables.syn1neg + hidden_output = model.syn1neg hidden_n, hidden_dim = hidden_output.shape fout.write(struct.pack('@2q', hidden_n, hidden_dim)) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index cc3be86da7..29e5cd2411 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -171,7 +171,7 @@ def count(self, new_val): class Doc2Vec(Word2Vec): def __init__(self, documents=None, corpus_file=None, vector_size=100, 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, callbacks=(), + dm_tag_count=1, dv=None, dv_mapfile=None, comment=None, trim_rule=None, callbacks=(), window=5, epochs=10, **kwargs): """Class for training, using and evaluating neural networks described in `Distributed Representations of Sentences and Documents `_. @@ -271,7 +271,7 @@ def __init__(self, documents=None, corpus_file=None, vector_size=100, dm_mean=No This object essentially contains the mapping between words and embeddings. After training, it can be used directly to query those embeddings in various ways. See the module level docstring for examples. - docvecs : :class:`~gensim.models.keyedvectors.KeyedVectors` + dv : :class:`~gensim.models.keyedvectors.KeyedVectors` This object contains the paragraph vectors learned from the training data. There will be one such vector for each unique document tag supplied during training. They may be individually accessed using the tag as an indexed-access key. For example, if one of the training documents used a tag of 'doc003': @@ -293,7 +293,7 @@ def __init__(self, documents=None, corpus_file=None, vector_size=100, dm_mean=No logger.info("using concatenative %d-dimensional layer1", self.layer1_size) self.vector_size = vector_size - self.dv = docvecs or KeyedVectors(self.vector_size, mapfile_path=docvecs_mapfile) + self.dv = dv or KeyedVectors(self.vector_size, mapfile_path=dv_mapfile) super(Doc2Vec, self).__init__( sentences=corpus_iterable, @@ -863,7 +863,7 @@ def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, prog """ total_words, corpus_count = self.scan_vocab( - corpus_iterable=corpus_iterable, corpus_file=corpus_file, docvecs=self.dv, + corpus_iterable=corpus_iterable, corpus_file=corpus_file, progress_per=progress_per, trim_rule=trim_rule ) self.corpus_count = corpus_count @@ -992,7 +992,7 @@ def _scan_vocab(self, corpus_iterable, progress_per, trim_rule): self.raw_vocab = vocab return total_words, corpus_count - def scan_vocab(self, corpus_iterable=None, corpus_file=None, docvecs=None, progress_per=10000, trim_rule=None): + def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=10000, trim_rule=None): """Create the models Vocabulary: A mapping from unique words in the corpus to their frequency count. Parameters @@ -1003,8 +1003,6 @@ def scan_vocab(self, corpus_iterable=None, corpus_file=None, docvecs=None, progr Path to a corpus file in :class:`~gensim.models.word2vec.LineSentence` format. You may use this argument instead of `documents` to get performance boost. Only one of `documents` or `corpus_file` arguments need to be passed (not both of them). - docvecs : list of :class:`~gensim.models.keyedvectors.KeyedVectors` - The vector representations of the documents in our corpus. Each of them has a size == `vector_size`. progress_per : int Progress will be logged every `progress_per` documents. trim_rule : function, optional diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index f0cb4fb403..4424849181 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -287,7 +287,8 @@ from gensim.models.word2vec import Word2Vec from gensim.models.keyedvectors import KeyedVectors -from gensim.utils import deprecated, call_on_class_only, open, NO_CYTHON +from gensim import utils +from gensim.utils import deprecated, call_on_class_only logger = logging.getLogger(__name__) @@ -303,7 +304,7 @@ ) from gensim.models.fasttext_corpusfile import train_epoch_sg, train_epoch_cbow except ImportError: - raise NO_CYTHON + raise utils.NO_CYTHON class FastText(Word2Vec): @@ -1047,7 +1048,7 @@ def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): The loaded model. """ - with open(model_file, 'rb') as fin: + with utils.open(model_file, 'rb') as fin: m = gensim.models._fasttext_bin.load(fin, encoding=encoding, full_model=full_model) model = FastText( @@ -1101,7 +1102,8 @@ def _check_model(m): 'mismatch between vector size in model params ({}) and model vectors ({})' .format(m.wv.vector_size, m.wv.vectors_ngrams) ) - if m.syn1neg is not None: + + if hasattr(m, 'syn1neg') and m.syn1neg is not None: assert m.wv.vector_size == m.syn1neg.shape[1], ( 'mismatch between vector size in model params ({}) and trainables ({})' .format(m.wv.vector_size, m.wv.vectors_ngrams) diff --git a/gensim/models/translation_matrix.py b/gensim/models/translation_matrix.py index 5aa9b42184..5801072bff 100644 --- a/gensim/models/translation_matrix.py +++ b/gensim/models/translation_matrix.py @@ -84,7 +84,7 @@ .. sourcecode:: pycon - >>> result = model_trans.infer_vector(dst_model.docvecs[data[3].tags]) + >>> result = model_trans.infer_vector(dst_model.dv[data[3].tags]) References @@ -392,7 +392,7 @@ class BackMappingTranslationMatrix(utils.SaveLoad): >>> model_trans = BackMappingTranslationMatrix(src_model, dst_model) >>> trans_matrix = model_trans.train(data) >>> - >>> result = model_trans.infer_vector(dst_model.docvecs[data[3].tags]) + >>> result = model_trans.infer_vector(dst_model.dv[data[3].tags]) """ def __init__(self, source_lang_vec, target_lang_vec, tagged_docs=None, random_state=None): @@ -436,8 +436,8 @@ def train(self, tagged_docs): Translation matrix that mapping from the source model's vector to target model's vector. """ - m1 = [self.source_lang_vec.docvecs[item.tags].flatten() for item in tagged_docs] - m2 = [self.target_lang_vec.docvecs[item.tags].flatten() for item in tagged_docs] + m1 = [self.source_lang_vec.dv[item.tags].flatten() for item in tagged_docs] + m2 = [self.target_lang_vec.dv[item.tags].flatten() for item in tagged_docs] self.translation_matrix = np.linalg.lstsq(m2, m1, -1)[0] return self.translation_matrix diff --git a/gensim/similarities/nmslib.py b/gensim/similarities/nmslib.py index b7ed1f0df4..d08b6b2d75 100644 --- a/gensim/similarities/nmslib.py +++ b/gensim/similarities/nmslib.py @@ -187,7 +187,7 @@ def _build_from_word2vec(self): def _build_from_doc2vec(self): """Build an NMSLIB index using document vectors from a Doc2Vec model.""" - docvecs = self.model.docvecs + docvecs = self.model.dv docvecs.init_sims() labels = [docvecs.index2key[i] for i in range(0, len(docvecs))] self._build_from_model(docvecs.vectors_norm, labels) diff --git a/gensim/sklearn_api/d2vmodel.py b/gensim/sklearn_api/d2vmodel.py index c49d1b2baf..370897bfdb 100644 --- a/gensim/sklearn_api/d2vmodel.py +++ b/gensim/sklearn_api/d2vmodel.py @@ -35,8 +35,8 @@ class D2VTransformer(TransformerMixin, BaseEstimator): `_. """ - def __init__(self, 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, vector_size=100, alpha=0.025, window=5, + def __init__(self, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1, dv=None, + dv_mapfile=None, comment=None, trim_rule=None, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, hs=0, negative=5, cbow_mean=1, hashfxn=hash, epochs=5, sorted_vocab=1, batch_words=10000): @@ -60,11 +60,10 @@ def __init__(self, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1 in the context strung together. dm_tag_count : int, optional Expected constant number of document tags per document, when using dm_concat mode. - docvecs : :class:`~gensim.models.keyedvectors.Doc2VecKeyedVectors` + dv : :class:`~gensim.models.keyedvectors.KeyedVectors` A mapping from a string or int tag to its vector representation. - Either this or `docvecs_mapfile` **MUST** be supplied. - docvecs_mapfile : str, optional - Path to a file containing the docvecs mapping. If `docvecs` is None, this file will be used to create it. + dv_mapfile : str, optional + Path to a file containing the docvecs mapping. If `dv` is None, this file will be used to create it. comment : str, optional A model descriptive comment, used for logging and debugging purposes. trim_rule : function ((str, int, int) -> int), optional @@ -123,8 +122,8 @@ def __init__(self, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1 self.dbow_words = dbow_words self.dm_concat = dm_concat self.dm_tag_count = dm_tag_count - self.docvecs = docvecs - self.docvecs_mapfile = docvecs_mapfile + self.dv = dv + self.dv_mapfile = dv_mapfile self.comment = comment self.trim_rule = trim_rule @@ -167,7 +166,7 @@ def fit(self, X, y=None): self.gensim_model = models.Doc2Vec( documents=d2v_sentences, dm_mean=self.dm_mean, dm=self.dm, dbow_words=self.dbow_words, dm_concat=self.dm_concat, dm_tag_count=self.dm_tag_count, - docvecs=self.docvecs, docvecs_mapfile=self.docvecs_mapfile, comment=self.comment, + dv=self.dv, dv_mapfile=self.dv_mapfile, comment=self.comment, trim_rule=self.trim_rule, vector_size=self.vector_size, alpha=self.alpha, window=self.window, min_count=self.min_count, max_vocab_size=self.max_vocab_size, sample=self.sample, seed=self.seed, workers=self.workers, min_alpha=self.min_alpha, hs=self.hs, diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index db581916ea..801ffc3bd9 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -90,12 +90,12 @@ def testPersistenceWord2VecFormat(self): test_doc_word = get_tmpfile('gensim_doc2vec.dw') model.save_word2vec_format(test_doc_word, doctag_vec=True, word_vec=True, binary=False) binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_doc_word, binary=False) - self.assertEqual(len(model.wv.vocab) + len(model.docvecs), len(binary_model_dv.vocab)) + self.assertEqual(len(model.wv.vocab) + len(model.dv), len(binary_model_dv.vocab)) # test saving document embedding only test_doc = get_tmpfile('gensim_doc2vec.d') model.save_word2vec_format(test_doc, doctag_vec=True, word_vec=False, binary=True) binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_doc, binary=True) - self.assertEqual(len(model.docvecs), len(binary_model_dv.vocab)) + self.assertEqual(len(model.dv), len(binary_model_dv.vocab)) # test saving word embedding only test_word = get_tmpfile('gensim_doc2vec.w') model.save_word2vec_format(test_word, doctag_vec=False, word_vec=True, binary=True) @@ -115,9 +115,9 @@ def obsolete_testLoadOldModel(self): self.assertTrue(model.wv.vectors_lockf.shape == (3955, )) self.assertTrue(model.cum_table.shape == (3955, )) - self.assertTrue(model.docvecs.vectors.shape == (300, 100)) - self.assertTrue(model.docvecs.vectors_lockf.shape == (300, )) - self.assertTrue(len(model.docvecs) == 300) + self.assertTrue(model.dv.vectors.shape == (300, 100)) + self.assertTrue(model.dv.vectors_lockf.shape == (300, )) + self.assertTrue(len(model.dv) == 300) self.model_sanity(model) @@ -134,9 +134,9 @@ def obsolete_testLoadOldModelSeparates(self): self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) self.assertTrue(model.wv.vectors_lockf.shape == (3955, )) self.assertTrue(model.cum_table.shape == (3955, )) - self.assertTrue(model.docvecs.vectors.shape == (300, 100)) - self.assertTrue(model.docvecs.vectors_lockf.shape == (300, )) - self.assertTrue(len(model.docvecs) == 300) + self.assertTrue(model.dv.vectors.shape == (300, 100)) + self.assertTrue(model.dv.vectors_lockf.shape == (300, )) + self.assertTrue(len(model.dv) == 300) self.model_sanity(model) @@ -192,18 +192,18 @@ def _check_old_version(self, old_version): self.assertTrue(len(model.wv.vocab) == 3) self.assertIsNone(model.corpus_total_words) self.assertTrue(model.wv.vectors.shape == (3, 4)) - self.assertTrue(model.docvecs.vectors.shape == (2, 4)) - self.assertTrue(len(model.docvecs) == 2) + self.assertTrue(model.dv.vectors.shape == (2, 4)) + self.assertTrue(len(model.dv) == 2) # check if inferring vectors for new documents and similarity search works. doc0_inferred = model.infer_vector(list(DocsLeeCorpus())[0].words) - sims_to_infer = model.docvecs.most_similar([doc0_inferred], topn=len(model.docvecs)) + sims_to_infer = model.dv.most_similar([doc0_inferred], topn=len(model.dv)) self.assertTrue(sims_to_infer) # check if inferring vectors and similarity search works after saving and loading back the model tmpf = get_tmpfile('gensim_doc2vec.tst') model.save(tmpf) loaded_model = doc2vec.Doc2Vec.load(tmpf) doc0_inferred = loaded_model.infer_vector(list(DocsLeeCorpus())[0].words) - sims_to_infer = loaded_model.docvecs.most_similar([doc0_inferred], topn=len(loaded_model.docvecs)) + sims_to_infer = loaded_model.dv.most_similar([doc0_inferred], topn=len(loaded_model.dv)) self.assertTrue(sims_to_infer) def testDoc2vecTrainParameters(self): @@ -330,9 +330,9 @@ def test_int_doctags(self): model = doc2vec.Doc2Vec(min_count=1) model.build_vocab(corpus) - self.assertEqual(len(model.docvecs.vectors), 300) - self.assertEqual(model.docvecs[0].shape, (100,)) - self.assertEqual(model.docvecs[np.int64(0)].shape, (100,)) + self.assertEqual(len(model.dv.vectors), 300) + self.assertEqual(model.dv[0].shape, (100,)) + self.assertEqual(model.dv[np.int64(0)].shape, (100,)) self.assertRaises(KeyError, model.__getitem__, '_*0') def test_missing_string_doctag(self): @@ -343,7 +343,7 @@ def test_missing_string_doctag(self): model = doc2vec.Doc2Vec(min_count=1) model.build_vocab(corpus) - self.assertRaises(KeyError, model.docvecs.__getitem__, 'not_a_tag') + self.assertRaises(KeyError, model.dv.__getitem__, 'not_a_tag') def test_string_doctags(self): """Test doc2vec doctag alternatives""" @@ -354,17 +354,17 @@ def test_string_doctags(self): model = doc2vec.Doc2Vec(min_count=1) model.build_vocab(corpus) - self.assertEqual(len(model.docvecs.vectors), 300) - self.assertEqual(model.docvecs[0].shape, (100,)) - self.assertEqual(model.docvecs['_*0'].shape, (100,)) - self.assertTrue(all(model.docvecs['_*0'] == model.docvecs[0])) - self.assertTrue(max(d.index for d in model.docvecs.map.values()) < len(model.docvecs.index2key)) + self.assertEqual(len(model.dv.vectors), 300) + self.assertEqual(model.dv[0].shape, (100,)) + self.assertEqual(model.dv['_*0'].shape, (100,)) + self.assertTrue(all(model.dv['_*0'] == model.dv[0])) + self.assertTrue(max(d.index for d in model.dv.map.values()) < len(model.dv.index2key)) self.assertLess( - max(model.docvecs.get_index(str_key) for str_key in model.docvecs.map.keys()), - len(model.docvecs.vectors) + max(model.dv.get_index(str_key) for str_key in model.dv.map.keys()), + len(model.dv.vectors) ) - # verify docvecs.most_similar() returns string doctags rather than indexes - self.assertEqual(model.docvecs.index2key[0], model.docvecs.most_similar([model.docvecs[0]])[0][0]) + # verify dv.most_similar() returns string doctags rather than indexes + self.assertEqual(model.dv.index2key[0], model.dv.most_similar([model.dv[0]])[0][0]) def test_empty_errors(self): # no input => "RuntimeError: you must first build vocabulary before training the model" @@ -394,18 +394,18 @@ def model_sanity(self, model, keep_training=True): # inferred vector should be top10 close to bulk-trained one doc0_inferred = model.infer_vector(list(DocsLeeCorpus())[0].words) - sims_to_infer = model.docvecs.most_similar([doc0_inferred], topn=len(model.docvecs)) + sims_to_infer = model.dv.most_similar([doc0_inferred], topn=len(model.dv)) f_rank = [docid for docid, sim in sims_to_infer].index(fire1) self.assertLess(f_rank, 10) # fire2 should be top30 close to fire1 - sims = model.docvecs.most_similar(fire1, topn=len(model.docvecs)) + sims = model.dv.most_similar(fire1, topn=len(model.dv)) f2_rank = [docid for docid, sim in sims].index(fire2) self.assertLess(f2_rank, 30) # same sims should appear in lookup by vec as by index - doc0_vec = model.docvecs[fire1] - sims2 = model.docvecs.most_similar(positive=[doc0_vec], topn=21) + doc0_vec = model.dv[fire1] + sims2 = model.dv.most_similar(positive=[doc0_vec], topn=21) sims2 = [(id, sim) for id, sim in sims2 if id != fire1] # ignore the doc itself sims = sims[:20] self.assertEqual(list(zip(*sims))[0], list(zip(*sims2))[0]) # same doc ids @@ -413,17 +413,17 @@ def model_sanity(self, model, keep_training=True): # sim results should be in clip range if given clip_sims = \ - model.docvecs.most_similar(fire1, clip_start=len(model.docvecs) // 2, clip_end=len(model.docvecs) * 2 // 3) + model.dv.most_similar(fire1, clip_start=len(model.dv) // 2, clip_end=len(model.dv) * 2 // 3) sims_doc_id = [docid for docid, sim in clip_sims] for s_id in sims_doc_id: - self.assertTrue(len(model.docvecs) // 2 <= s_id <= len(model.docvecs) * 2 // 3) + self.assertTrue(len(model.dv) // 2 <= s_id <= len(model.dv) * 2 // 3) # fire docs should be closer than fire-alt - self.assertLess(model.docvecs.similarity(fire1, alt1), model.docvecs.similarity(fire1, fire2)) - self.assertLess(model.docvecs.similarity(fire2, alt1), model.docvecs.similarity(fire1, fire2)) + self.assertLess(model.dv.similarity(fire1, alt1), model.dv.similarity(fire1, fire2)) + self.assertLess(model.dv.similarity(fire2, alt1), model.dv.similarity(fire1, fire2)) # alt doc should be out-of-place among fire news - self.assertEqual(model.docvecs.doesnt_match([fire1, alt1, fire2]), alt1) + self.assertEqual(model.dv.doesnt_match([fire1, alt1, fire2]), alt1) # keep training after save if keep_training: @@ -437,7 +437,7 @@ def test_training(self): corpus = DocsLeeCorpus() model = doc2vec.Doc2Vec(vector_size=100, min_count=2, epochs=20, workers=1) model.build_vocab(corpus) - self.assertEqual(model.docvecs.vectors.shape, (300, 100)) + self.assertEqual(model.dv.vectors.shape, (300, 100)) model.train(corpus, total_examples=model.corpus_count, epochs=model.epochs) self.model_sanity(model) @@ -454,7 +454,7 @@ def test_training_fromfile(self): model = doc2vec.Doc2Vec(vector_size=100, min_count=2, epochs=20, workers=1) model.build_vocab(corpus_file=corpus_file) - self.assertEqual(model.docvecs.vectors.shape, (300, 100)) + self.assertEqual(model.dv.vectors.shape, (300, 100)) model.train(corpus_file=corpus_file, total_words=model.corpus_total_words, epochs=model.epochs) self.model_sanity(model) @@ -643,8 +643,8 @@ def test_mixed_tag_types(self): mixed_tag_corpus = [doc2vec.TaggedDocument(words, [i, words[0]]) for i, words in enumerate(raw_sentences)] model = doc2vec.Doc2Vec() model.build_vocab(mixed_tag_corpus) - expected_length = len(sentences) + len(model.docvecs.map) # 9 sentences, 7 unique first tokens - self.assertEqual(len(model.docvecs.vectors), expected_length) + expected_length = len(sentences) + len(model.dv.map) # 9 sentences, 7 unique first tokens + self.assertEqual(len(model.dv.vectors), expected_length) def models_equal(self, model, model2): # check words/hidden-weights @@ -655,8 +655,8 @@ def models_equal(self, model, model2): if model.negative: self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) # check docvecs - self.assertEqual(len(model.docvecs.map), len(model2.docvecs.map)) - self.assertEqual(len(model.docvecs.index2key), len(model2.docvecs.index2key)) + self.assertEqual(len(model.dv.map), len(model2.dv.map)) + self.assertEqual(len(model.dv.index2key), len(model2.dv.index2key)) def test_word_vec_non_writeable(self): model = keyedvectors.KeyedVectors.load_word2vec_format(datapath('word2vec_pre_kv_c')) @@ -716,8 +716,8 @@ class ConcatenatedDoc2Vec(object): def __init__(self, models): self.models = models - if hasattr(models[0], 'docvecs'): - self.docvecs = ConcatenatedDocvecs([model.docvecs for model in models]) + if hasattr(models[0], 'dv'): + self.dv = ConcatenatedDocvecs([model.dv for model in models]) def __getitem__(self, token): return np.concatenate([model[token] for model in self.models]) diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index dbd37a025e..87497c9dbf 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -16,7 +16,7 @@ from gensim import utils from gensim.models.word2vec import LineSentence -from gensim.models.fasttext import FastText as FT_gensim, FastTextKeyedVectors, _unpack, _unpack_copy +from gensim.models.fasttext import FastText as FT_gensim, FastTextKeyedVectors, _unpack from gensim.models.keyedvectors import KeyedVectors from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences import gensim.models._fasttext_bin @@ -825,61 +825,6 @@ def obsolete_testLoadOldModel(self): self.assertEqual(model.wv.vectors_vocab.shape, (12, 100)) self.assertEqual(model.wv.vectors_ngrams.shape, (2000000, 100)) - def compare_with_wrapper(self, model_gensim, model_wrapper): - # make sure we get >=2 overlapping words for top-10 similar words suggested for `night` - sims_gensim = model_gensim.wv.most_similar('night', topn=10) - sims_gensim_words = (list(map(lambda x: x[0], sims_gensim))) # get similar words - - sims_wrapper = model_wrapper.most_similar('night', topn=10) - sims_wrapper_words = (list(map(lambda x: x[0], sims_wrapper))) # get similar words - - overlap_count = len(set(sims_gensim_words).intersection(sims_wrapper_words)) - - # overlap increases as we increase `iter` value, min overlap set to 2 to avoid unit-tests taking too long - # this limit can be increased when using Cython code - self.assertGreaterEqual(overlap_count, 2) - - @unittest.skipIf(not FT_HOME, "FT_HOME env variable not set, skipping test") - def test_cbow_hs_against_wrapper(self): - tmpf = get_tmpfile('gensim_fasttext.tst') - model_wrapper = FT_wrapper.train(ft_path=FT_CMD, corpus_file=datapath('lee_background.cor'), - output_file=tmpf, model='cbow', vector_size=50, alpha=0.05, window=5, min_count=5, - word_ngrams=1, - loss='hs', sample=1e-3, negative=0, epochs=5, min_n=3, max_n=6, sorted_vocab=1, - threads=12) - - model_gensim = FT_gensim(vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, - sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) - - lee_data = LineSentence(datapath('lee_background.cor')) - model_gensim.build_vocab(lee_data) - orig0 = np.copy(model_gensim.wv.vectors[0]) - model_gensim.train(lee_data, total_examples=model_gensim.corpus_count, epochs=model_gensim.epochs) - self.assertFalse((orig0 == model_gensim.wv.vectors[0]).all()) # vector should vary after training - self.compare_with_wrapper(model_gensim, model_wrapper) - - @unittest.skipIf(not FT_HOME, "FT_HOME env variable not set, skipping test") - def test_sg_hs_against_wrapper(self): - - tmpf = get_tmpfile('gensim_fasttext.tst') - model_wrapper = FT_wrapper.train(ft_path=FT_CMD, corpus_file=datapath('lee_background.cor'), - output_file=tmpf, model='skipgram', vector_size=48, alpha=0.025, window=5, - min_count=5, word_ngrams=1, - loss='hs', sample=1e-3, negative=0, epochs=5, min_n=3, max_n=6, sorted_vocab=1, - threads=12) - - model_gensim = FT_gensim(vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, - sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) - - lee_data = LineSentence(datapath('lee_background.cor')) - model_gensim.build_vocab(lee_data) - orig0 = np.copy(model_gensim.wv.vectors[0]) - model_gensim.train(lee_data, total_examples=model_gensim.corpus_count, epochs=model_gensim.epochs) - self.assertFalse((orig0 == model_gensim.wv.vectors[0]).all()) # vector should vary after training - self.compare_with_wrapper(model_gensim, model_wrapper) - with open(datapath('toy-data.txt')) as fin: TOY_SENTENCES = [fin.read().strip().split(' ')] @@ -1632,10 +1577,10 @@ def _check_roundtrip(self, sg): self.assertEqual(model_trained.negative, model_loaded.negative) self.assertEqual(model_trained.hs, model_loaded.hs) self.assertEqual(model_trained.sg, model_loaded.sg) - self.assertEqual(model_trained.trainables.bucket, model_loaded.trainables.bucket) + self.assertEqual(model_trained.bucket, model_loaded.bucket) self.assertEqual(model_trained.wv.min_n, model_loaded.wv.min_n) self.assertEqual(model_trained.wv.max_n, model_loaded.wv.max_n) - self.assertEqual(model_trained.vocabulary.sample, model_loaded.vocabulary.sample) + self.assertEqual(model_trained.sample, model_loaded.sample) self.assertEqual(set(model_trained.wv.index2word), set(model_loaded.wv.index2word)) for w in model_trained.wv.index2word: diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py index e56da9a9e4..8a0f15b3f0 100644 --- a/gensim/test/test_keyedvectors.py +++ b/gensim/test/test_keyedvectors.py @@ -12,10 +12,9 @@ import logging import unittest -from mock import patch import numpy as np -from gensim.models.keyedvectors import KeyedVectors, FastTextKeyedVectors, REAL +from gensim.models.keyedvectors import KeyedVectors, REAL from gensim.test.utils import datapath import gensim.models.keyedvectors diff --git a/gensim/test/test_similarities.py b/gensim/test/test_similarities.py index 1b4c0174a4..e5ab6a10ac 100644 --- a/gensim/test/test_similarities.py +++ b/gensim/test/test_similarities.py @@ -655,9 +655,9 @@ def setUp(self): from gensim.similarities.index import AnnoyIndexer self.model = doc2vec.Doc2Vec(sentences, min_count=1) - self.model.docvecs.init_sims() + self.model.dv.init_sims() self.index = AnnoyIndexer(self.model, 300) - self.vector = self.model.docvecs.vectors_norm[0] + self.vector = self.model.dv.vectors_norm[0] def testDocumentIsSimilarToItself(self): approx_neighbors = self.index.most_similar(self.vector, 1) @@ -667,8 +667,8 @@ def testDocumentIsSimilarToItself(self): self.assertAlmostEqual(similarity, 1.0, places=2) def testApproxNeighborsMatchExact(self): - approx_neighbors = self.model.docvecs.most_similar([self.vector], topn=5, indexer=self.index) - exact_neighbors = self.model.docvecs.most_similar( + approx_neighbors = self.model.dv.most_similar([self.vector], topn=5, indexer=self.index) + exact_neighbors = self.model.dv.most_similar( positive=[self.vector], topn=5) approx_words = [neighbor[0] for neighbor in approx_neighbors] @@ -807,9 +807,9 @@ def setUp(self): from gensim.similarities.nmslib import NmslibIndexer self.model = doc2vec.Doc2Vec(sentences, min_count=1) - self.model.docvecs.init_sims() + self.model.dv.init_sims() self.index = NmslibIndexer(self.model) - self.vector = self.model.docvecs.vectors_norm[0] + self.vector = self.model.dv.vectors_norm[0] def test_document_is_similar_to_itself(self): approx_neighbors = self.index.most_similar(self.vector, 1) @@ -819,8 +819,8 @@ def test_document_is_similar_to_itself(self): self.assertAlmostEqual(similarity, 1.0, places=2) def test_approx_neighbors_match_exact(self): - approx_neighbors = self.model.docvecs.most_similar([self.vector], topn=5, indexer=self.index) - exact_neighbors = self.model.docvecs.most_similar( + approx_neighbors = self.model.dv.most_similar([self.vector], topn=5, indexer=self.index) + exact_neighbors = self.model.dv.most_similar( positive=[self.vector], topn=5) approx_words = [neighbor[0] for neighbor in approx_neighbors] diff --git a/gensim/test/test_translation_matrix.py b/gensim/test/test_translation_matrix.py index 2841845e6c..8846dc617d 100644 --- a/gensim/test/test_translation_matrix.py +++ b/gensim/test/test_translation_matrix.py @@ -111,7 +111,7 @@ def test_infer_vector(self): self.source_doc_vec, self.target_doc_vec, self.train_docs[:5] ) model.train(self.train_docs[:5]) - backmapped_vec = model.infer_vector(self.target_doc_vec.docvecs[self.train_docs[5].tags]) + backmapped_vec = model.infer_vector(self.target_doc_vec.dv[self.train_docs[5].tags]) self.assertEqual(backmapped_vec.shape, (8, )) d2v_inferred_vector = self.source_doc_vec.infer_vector(self.train_docs[5].words) From 79af68e5f7bcbd231fb9414d58291055e8938eb5 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 11 May 2020 19:23:27 -0700 Subject: [PATCH 24/60] intensify FT tests (more epochs, more buckets) --- gensim/test/test_fasttext.py | 74 ++++++++++++++++++++++++------------ 1 file changed, 49 insertions(+), 25 deletions(-) diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index 87497c9dbf..094c987e3d 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -37,7 +37,7 @@ # Limit the size of FastText ngram buckets, for RAM reasons. # See https://github.com/RaRe-Technologies/gensim/issues/2790 -BUCKET = 5000 +BUCKET = 10000 FT_HOME = os.environ.get("FT_HOME") FT_CMD = os.path.join(FT_HOME, "fasttext") if FT_HOME else None @@ -418,7 +418,7 @@ def test_cbow_hs_training(self): model_gensim = FT_gensim( vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -440,14 +440,17 @@ def test_cbow_hs_training(self): u'flights', u'during', u'comes'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_cbow_hs_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) lee_data = LineSentence(datapath('lee_background.cor')) @@ -473,14 +476,17 @@ def test_cbow_hs_training_fromfile(self): u'flights', u'during', u'comes'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_sg_hs_training(self): model_gensim = FT_gensim( vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -502,14 +508,17 @@ def test_sg_hs_training(self): u'manslaughter', u'north', u'flight'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_sg_hs_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=1, negative=0, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -535,14 +544,17 @@ def test_sg_hs_training_fromfile(self): u'manslaughter', u'north', u'flight'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_cbow_neg_training(self): model_gensim = FT_gensim( vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -564,14 +576,17 @@ def test_cbow_neg_training(self): u'remains', u'overnight', u'running'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_cbow_neg_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( vector_size=48, sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=5, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET) lee_data = LineSentence(datapath('lee_background.cor')) @@ -597,14 +612,17 @@ def test_cbow_neg_training_fromfile(self): u'remains', u'overnight', u'running'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_sg_neg_training(self): model_gensim = FT_gensim( vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) lee_data = LineSentence(datapath('lee_background.cor')) @@ -626,14 +644,17 @@ def test_sg_neg_training(self): u'firm', u'singles', u'death'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_sg_neg_training_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext.tst')) as corpus_file: model_gensim = FT_gensim( vector_size=48, sg=1, cbow_mean=1, alpha=0.025, window=5, hs=0, negative=5, - min_count=5, epochs=5, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, + min_count=5, epochs=10, batch_words=1000, word_ngrams=1, sample=1e-3, min_n=3, max_n=6, sorted_vocab=1, workers=1, min_alpha=0.0, bucket=BUCKET * 4) lee_data = LineSentence(datapath('lee_background.cor')) @@ -659,8 +680,11 @@ def test_sg_neg_training_fromfile(self): u'firm', u'singles', u'death'] - overlap_count = len(set(sims_gensim_words).intersection(expected_sims_words)) - self.assertGreaterEqual(overlap_count, 2) + overlaps = set(sims_gensim_words).intersection(expected_sims_words) + overlap_count = len(overlaps) + self.assertGreaterEqual( + overlap_count, 2, + "only %i overlap in expected %s & actual %s" % (overlap_count, expected_sims_words, sims_gensim_words)) def test_online_learning(self): model_hs = FT_gensim(sentences, vector_size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) From 8875d8b80da18c09375f6646a7f71e93c17437e4 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 11 May 2020 20:35:49 -0700 Subject: [PATCH 25/60] flake8-3.8.0 style fixes - but also pin flake8-3.7.9 vs 3.8.0 'output_file' error --- gensim/corpora/sharded_corpus.py | 4 ++-- gensim/models/fasttext.py | 2 +- gensim/test/test_doc2vec.py | 8 ++++---- gensim/test/test_fasttext.py | 10 +++++----- gensim/test/test_word2vec.py | 18 +++++++++--------- tox.ini | 3 ++- 6 files changed, 23 insertions(+), 22 deletions(-) diff --git a/gensim/corpora/sharded_corpus.py b/gensim/corpora/sharded_corpus.py index a8d8e498fa..4b30b4ec7b 100644 --- a/gensim/corpora/sharded_corpus.py +++ b/gensim/corpora/sharded_corpus.py @@ -687,8 +687,8 @@ def __add_to_slice(self, s_result, result_start, result_stop, start, stop): """ if (result_stop - result_start) != (stop - start): raise ValueError( - 'Result start/stop range different than stop/start range (%d - %d vs. %d - %d)' - % (result_start, result_stop, start, stop) + 'Result start/stop range different than stop/start range ({0} - {1} vs. {2} - {3})' + .format(result_start, result_stop, start, stop) ) # Dense data: just copy using numpy's slice notation diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 4424849181..06593e78f0 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -1644,5 +1644,5 @@ def ft_ngram_hashes(word, minn, maxn, num_buckets, fb_compatible=True): # BACKWARD COMPATIBILITY FOR OLDER PICKLES -from gensim.models import keyedvectors # noqa: F402 +from gensim.models import keyedvectors # noqa: E402 keyedvectors.FastTextKeyedVectors = FastTextKeyedVectors diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index 801ffc3bd9..814ebec002 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -665,17 +665,17 @@ def test_word_vec_non_writeable(self): vector *= 0 @log_capture() - def testBuildVocabWarning(self, line): + def testBuildVocabWarning(self, loglines): """Test if logger warning is raised on non-ideal input to a doc2vec model""" raw_sentences = ['human', 'machine'] sentences = [doc2vec.TaggedDocument(words, [i]) for i, words in enumerate(raw_sentences)] model = doc2vec.Doc2Vec() model.build_vocab(sentences) warning = "Each 'words' should be a list of words (usually unicode strings)." - self.assertTrue(warning in str(line)) + self.assertTrue(warning in str(loglines)) @log_capture() - def testTrainWarning(self, line): + def testTrainWarning(self, loglines): """Test if warning is raised if alpha rises during subsequent calls to train()""" raw_sentences = [['human'], ['graph', 'trees']] @@ -689,7 +689,7 @@ def testTrainWarning(self, line): if epoch == 5: model.alpha += 0.05 warning = "Effective 'alpha' higher than previous training cycles" - self.assertTrue(warning in str(line)) + self.assertTrue(warning in str(loglines)) def testLoadOnClassError(self): """Test if exception is raised when loading doc2vec model on instance""" diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index 094c987e3d..29fbcf98c3 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -739,12 +739,12 @@ def test_online_learning_after_save_fromfile(self): def online_sanity(self, model): terro, others = [], [] - for x in list_corpus: - if 'terrorism' in x: - terro.append(x) + for line in list_corpus: + if 'terrorism' in line: + terro.append(line) else: - others.append(x) - self.assertTrue(all('terrorism' not in x for x in others)) + others.append(line) + self.assertTrue(all('terrorism' not in line for line in others)) model.build_vocab(others) start_vecs = model.wv.vectors_vocab.copy() model.train(others, total_examples=model.corpus_count, epochs=model.epochs) diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 062df792ca..7bbbd15aa6 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -244,12 +244,12 @@ def testOnlineLearningAfterSaveFromFile(self): def onlineSanity(self, model, trained_model=False): terro, others = [], [] - for x in list_corpus: - if 'terrorism' in x: - terro.append(x) + for line in list_corpus: + if 'terrorism' in line: + terro.append(line) else: - others.append(x) - self.assertTrue(all('terrorism' not in x for x in others)) + others.append(line) + self.assertTrue(all('terrorism' not in line for line in others)) model.build_vocab(others, update=trained_model) model.train(others, total_examples=model.corpus_count, epochs=model.epochs) self.assertFalse('terrorism' in model.wv.vocab) @@ -974,16 +974,16 @@ def _check_old_version(self, old_version): loaded_model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) @log_capture() - def testBuildVocabWarning(self, line): + def testBuildVocabWarning(self, loglines): """Test if warning is raised on non-ideal input to a word2vec model""" sentences = ['human', 'machine'] model = word2vec.Word2Vec() model.build_vocab(sentences) warning = "Each 'sentences' item should be a list of words (usually unicode strings)." - self.assertTrue(warning in str(line)) + self.assertTrue(warning in str(loglines)) @log_capture() - def testTrainWarning(self, line): + def testTrainWarning(self, loglines): """Test if warning is raised if alpha rises during subsequent calls to train()""" sentences = [ ['human'], @@ -998,7 +998,7 @@ def testTrainWarning(self, line): if epoch == 5: model.alpha += 0.05 warning = "Effective 'alpha' higher than previous training cycles" - self.assertTrue(warning in str(line)) + self.assertTrue(warning in str(loglines)) def test_train_with_explicit_param(self): model = word2vec.Word2Vec(vector_size=2, min_count=1, hs=1, negative=0) diff --git a/tox.ini b/tox.ini index 927a8deacc..1d0c0b0e09 100644 --- a/tox.ini +++ b/tox.ini @@ -50,7 +50,8 @@ commands = [testenv:flake8] recreate = True -deps = flake8 +deps = + flake8==3.7.9 # 3.8.0 triggers "AttributeError: 'Namespace' object has no attribute 'output_file'" commands = flake8 gensim/ {posargs} From 4b7566ea5694954fb6a5201379e0d8f7b164f380 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 11 May 2020 21:55:37 -0700 Subject: [PATCH 26/60] replace vectors_norm with 1d norms --- gensim/models/doc2vec.py | 8 +-- gensim/models/fasttext.py | 3 +- gensim/models/keyedvectors.py | 108 +++++++++++++++++++--------------- gensim/models/word2vec.py | 20 ++----- gensim/similarities/docsim.py | 16 ++--- gensim/similarities/nmslib.py | 3 - gensim/test/test_fasttext.py | 17 +----- gensim/test/test_word2vec.py | 29 +-------- 8 files changed, 77 insertions(+), 127 deletions(-) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 29e5cd2411..32d5ccce06 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -333,13 +333,9 @@ def docvecs(self, value): self.dv = value def _clear_post_train(self): - """Alias for :meth:`~gensim.models.doc2vec.Doc2Vec.clear_sims`.""" - self.clear_sims() - - def clear_sims(self): """Resets the current word vectors. """ - self.wv.vectors_norm = None - self.dv.vectors_norm = None + self.wv.norms = None + self.dv.norms = None def reset_weights(self): super(Doc2Vec, self).reset_weights() diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 06593e78f0..6aebe01e76 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -842,7 +842,7 @@ def save(self, *args, **kwargs): """ kwargs['ignore'] = kwargs.get( - 'ignore', ['vectors_norm', 'buckets_word']) + 'ignore', []) + ['buckets_word', ] super(FastText, self).save(*args, **kwargs) @classmethod @@ -1280,7 +1280,6 @@ def save(self, *args, **kwargs): """ # don't bother storing the cached normalized vectors ignore_attrs = [ - 'vectors_norm', 'buckets_word', 'hash2index', ] diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 87b0fe771a..ad7038a6c5 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -175,7 +175,7 @@ from Queue import Queue, Empty # noqa:F401 from numpy import dot, float32 as REAL, \ - double, array, zeros, vstack, sqrt, newaxis, \ + double, array, zeros, vstack, \ ndarray, sum as np_sum, prod, argmax, dtype, ascontiguousarray, \ frombuffer import numpy as np @@ -203,7 +203,7 @@ def __init__(self, vector_size, mapfile_path=None): """ self.vectors = zeros((0, vector_size), dtype=REAL) # fka (formerly known as) syn0 - self.vectors_norm = None # fka syn0norm + self.norms = None self.map = {} self.vector_size = vector_size self.index2key = [] # fka index2entity or index2word @@ -220,8 +220,10 @@ def _load_specials(self, *args, **kwargs): # fixup rename into vectors of older syn0 if not hasattr(self, 'vectors'): self.vectors = self.__dict__.pop('syn0', None) - self.vectors_norm = None self.vector_size = self.vectors.shape[1] + # ensure at least a 'None' in 'norms' to force recalc + if not hasattr(self, 'norms'): + self.norms = None # fixup rename of vocab into map if 'map' not in self.__dict__: self.map = self.__dict__.pop('vocab', None) @@ -238,7 +240,7 @@ def resize_vectors(self): else: self.vectors = np.empty((target_count, self.vector_size), dtype=REAL) self.vectors[0:min(prev_count, target_count), ] = prev_vectors[0:min(prev_count, target_count), ] - self.vectors_norm = None + self.norms = None return range(prev_count, target_count) def randomly_initialize_vectors(self, indexes=None, seed=0): @@ -251,7 +253,7 @@ def randomly_initialize_vectors(self, indexes=None, seed=0): for i in indexes: self.vectors[i] = pseudorandom_weak_vector(self.vectors.shape[1], seed_string=(str(self.index2key[i]) + str(seed))) - self.vectors_norm = None + self.norms = None def __len__(self): return len(self.index2key) @@ -310,7 +312,7 @@ def get_vector(self, key, use_norm=False): """ index = self.get_index(key) if use_norm: - result = self.vectors_norm[index] + result = self.vectors[index] / self.norms[index] else: result = self.vectors[index] @@ -418,6 +420,26 @@ def rank(self, key1, key2): return len(self.closer_than(key1, key2)) + 1 # backward compatibility + @property + def vectors_norm(self): + self.fill_norms() + return self.vectors / self.norms[..., np.newaxis] + + def fill_norms(self, force=False): + """ + Ensure per-vector norms are available. + + (Any code which modifies vectors should ensure the + accompanying norms are recalculated, or 'None'-out + 'norms' to trigger full recalc later.) + """ + if self.norms is None or force: + self.norms = np.linalg.norm(self.vectors, axis=1) + + @vectors_norm.setter + def vectors_norm(self, _): + pass # no-op; shouldn't be set + @property def index2entity(self): return self.index2key @@ -456,8 +478,6 @@ def save(self, *args, **kwargs): Load saved model. """ - # don't bother storing the cached normalized vectors - kwargs['ignore'] = kwargs.get('ignore', ['vectors_norm']) super(KeyedVectors, self).save(*args, **kwargs) def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip_end=None, @@ -506,8 +526,8 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip if negative is None: negative = [] - self.init_sims() - clip_end = clip_end or len(self.vectors_norm) + self.fill_norms() + clip_end = clip_end or len(self.vectors) if restrict_vocab: clip_start = 0 @@ -543,7 +563,7 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip if indexer is not None and isinstance(topn, int): return indexer.most_similar(mean, topn) - dists = dot(self.vectors_norm[clip_start:clip_end], mean) + dists = dot(self.vectors[clip_start:clip_end], mean) / self.norms[clip_start:clip_end] if not topn: return dists best = matutils.argsort(dists, topn=topn + len(all_keys), reverse=True) @@ -688,8 +708,9 @@ def wmdistance(self, document1, document2): if t2 not in docset2 or distance_matrix[i, j] != 0.0: continue - # Compute Euclidean distance between word vectors. - distance_matrix[i, j] = distance_matrix[j, i] = sqrt(np_sum((self[t1] - self[t2])**2)) + # Compute Euclidean distance between unit-normed word vectors. + distance_matrix[i, j] = distance_matrix[j, i] = np.sqrt( + np_sum((self.get_vector(t1, use_norm=True) - self.get_vector(t2, use_norm=True))**2)) if np_sum(distance_matrix) == 0.0: # `emd` gets stuck if the distance matrix contains only zeros. @@ -751,7 +772,7 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): if negative is None: negative = [] - self.init_sims() + self.fill_norms() if isinstance(positive, string_types) and not negative: # allow calls like most_similar_cosmul('dog'), as a shorthand for most_similar_cosmul(['dog']) @@ -776,8 +797,8 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): # 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] + pos_dists = [((1 + dot(self.vectors, term) / self.norms) / 2) for term in positive] + neg_dists = [((1 + dot(self.vectors, term) / self.norms) / 2) for term in negative] dists = prod(pos_dists, axis=0) / (prod(neg_dists, axis=0) + 0.000001) if not topn: @@ -801,7 +822,7 @@ def doesnt_match(self, words): The key further away from the mean of all keys. """ - self.init_sims() + self.fill_norms() used_words = [word for word in words if word in self] if len(used_words) != len(words): @@ -1179,25 +1200,37 @@ def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, self.log_evaluate_word_pairs(pearson, spearman, oov_ratio, pairs) return pearson, spearman, oov_ratio + @deprecated("use fill_norms instead") def init_sims(self, replace=False): - """Precompute L2-normalized vectors. + """Precompute data helpful for bulk similarity calculations. + + :meth:`~gensim.models.keyedvectors.KeyedVectors.fill_norms` now preferred for this purpose. Parameters ---------- replace : bool, optional - If True - forget the original vectors and only keep the normalized ones = saves lots of memory! + If True - forget the original vectors and only keep the normalized ones. Warnings -------- - You **cannot continue training** after doing a replace. - The model becomes effectively read-only: you can call - :meth:`~gensim.models.keyedvectors.KeyedVectors.most_similar`, - :meth:`~gensim.models.keyedvectors.KeyedVectors.similarity`, etc., but not train. + You **cannot sensibly continue training** after doing a replace on a model's + internal KeyedVectors, and a replace is no longer necessary to save RAM. + + """ + self.fill_norms() + if replace: + logger.warning("destructive init_sims(replace=True) deprecated & no longer required for space-efficiency") + self.unit_normalize_all() + + def unit_normalize_all(self): + """Destructively scale all vectors to unit-length. + + (You cannot sensibly continue training after such a step.) """ - if getattr(self, 'vectors_norm', None) is None or replace: - logger.info("precomputing L2-norms of key weight vectors") - self.vectors_norm = _l2_norm(self.vectors, replace=replace) + self.fill_norms() + self.vectors /= self.norms[..., np.newaxis] + self.norms = np.ones((len(self.vectors),)) def relative_cosine_similarity(self, wa, wb, topn=10): """Compute the relative cosine similarity between two words given top-n similar words, @@ -1388,29 +1421,6 @@ def similarity_unseen_docs(self, *args, **kwargs): EuclideanKeyedVectors = KeyedVectors -def _l2_norm(m, replace=False): - """Return an L2-normalized version of a matrix. - - Parameters - ---------- - m : np.array - The matrix to normalize. - replace : boolean, optional - If True, modifies the existing matrix. - - Returns - ------- - The normalized matrix. If replace=True, this will be the same as m. - - """ - dist = sqrt((m ** 2).sum(-1))[..., newaxis] - if replace: - m /= dist - return m - else: - return (m / dist).astype(REAL) - - @dataclass class SimpleVocab: """A single vocabulary item, used internally for collecting per-word position in the diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 5432059ec4..9c5b033c7b 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -849,7 +849,7 @@ def update_weights(self): if self.negative: pad = zeros((gained_vocab, self.layer1_size), dtype=REAL) self.syn1neg = vstack([self.syn1neg, pad]) - self.wv.vectors_norm = None + self.wv.norms = None # do not suppress learning for already learned words self.wv.vectors_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning @@ -894,8 +894,8 @@ def _do_train_job(self, sentences, alpha, inits): return tally, self._raw_word_count(sentences) def _clear_post_train(self): - """Remove all L2-normalized word vectors from the model.""" - self.wv.vectors_norm = None + """Clear any cached vector lengths from the model.""" + self.wv.norms = None def train(self, corpus_iterable=None, corpus_file=None, total_examples=None, total_words=None, epochs=None, start_alpha=None, end_alpha=None, word_count=0, @@ -1735,21 +1735,13 @@ def worker_loop(): pass # already out of loop; continue to next push elapsed = default_timer() - start - self.clear_sims() + self.wv.norms = None # clear any cached lengths logger.info( "scoring %i sentences took %.1fs, %.0f sentences/s", sentence_count, elapsed, sentence_count / elapsed ) return sentence_scores[:sentence_count] - def clear_sims(self): - """Remove all L2-normalized word vectors from the model, to free up memory. - - You can recompute them later again using the :meth:`~gensim.models.word2vec.Word2Vec.init_sims` method. - - """ - self.wv.vectors_norm = None - def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='utf8', unicode_errors='strict'): """Merge in an input-hidden weight matrix loaded from the original C word2vec-tool format, where it intersects with the current vocabulary. @@ -1901,8 +1893,8 @@ def save(self, *args, **kwargs): Path to the file. """ - # don't bother storing the cached normalized vectors, recalculable table - kwargs['ignore'] = kwargs.get('ignore', ['vectors_norm', 'cum_table']) + # don't bother storing recalculable table + kwargs['ignore'] = kwargs.get('ignore', []) + ['cum_table', ] super(Word2Vec, self).save(*args, **kwargs) def get_latest_training_loss(self): diff --git a/gensim/similarities/docsim.py b/gensim/similarities/docsim.py index 316b3a4c28..daba706eb1 100755 --- a/gensim/similarities/docsim.py +++ b/gensim/similarities/docsim.py @@ -1022,25 +1022,23 @@ class WmdSimilarity(interfaces.SimilarityABC): >>> sims = index[query] """ - def __init__(self, corpus, w2v_model, num_best=None, normalize_w2v_and_replace=True, chunksize=256): + def __init__(self, corpus, kv_model, num_best=None, chunksize=256): """ Parameters ---------- corpus: iterable of list of str A list of documents, each of which is a list of tokens. - w2v_model: :class:`~gensim.models.word2vec.Word2VecTrainables` - A trained word2vec model. + kv_model: :class:`~gensim.models.keyedvectors.KeyedVectors` + A set of KeyedVectors num_best: int, optional Number of results to retrieve. - normalize_w2v_and_replace: bool, optional - Whether or not to normalize the word2vec vectors to length 1. chunksize : int, optional Size of chunk. """ self.corpus = corpus - self.w2v_model = w2v_model + self.wv = kv_model self.num_best = num_best self.chunksize = chunksize @@ -1050,10 +1048,6 @@ def __init__(self, corpus, w2v_model, num_best=None, normalize_w2v_and_replace=T # index is simply an array from 0 to size of corpus. self.index = numpy.arange(len(corpus)) - if normalize_w2v_and_replace: - # Normalize vectors in word2vec class to length 1. - w2v_model.wv.init_sims(replace=True) - def __len__(self): """Get size of corpus.""" return len(self.corpus) @@ -1087,7 +1081,7 @@ def get_similarities(self, query): result = [] for qidx in range(n_queries): # Compute similarity for each query. - qresult = [self.w2v_model.wv.wmdistance(document, query[qidx]) for document in self.corpus] + qresult = [self.wv.wmdistance(document, query[qidx]) for document in self.corpus] qresult = numpy.array(qresult) qresult = 1. / (1. + qresult) # Similarity is the negative of the distance. diff --git a/gensim/similarities/nmslib.py b/gensim/similarities/nmslib.py index d08b6b2d75..be0aedba28 100644 --- a/gensim/similarities/nmslib.py +++ b/gensim/similarities/nmslib.py @@ -181,21 +181,18 @@ def load(cls, fname): def _build_from_word2vec(self): """Build an NMSLIB index using word vectors from a Word2Vec model.""" - self.model.wv.init_sims() self._build_from_model(self.model.wv.vectors_norm, self.model.wv.index2word) def _build_from_doc2vec(self): """Build an NMSLIB index using document vectors from a Doc2Vec model.""" docvecs = self.model.dv - docvecs.init_sims() labels = [docvecs.index2key[i] for i in range(0, len(docvecs))] self._build_from_model(docvecs.vectors_norm, labels) def _build_from_keyedvectors(self): """Build an NMSLIB index using word vectors from a KeyedVectors model.""" - self.model.init_sims() self._build_from_model(self.model.vectors_norm, self.model.index2word) def _build_from_model(self, vectors, labels): diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index 29fbcf98c3..e3a023ee6a 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -84,7 +84,7 @@ def test_training(self): self.model_sanity(model) # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.get_vector('graph', use_norm=True) sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -129,7 +129,7 @@ def test_training_fromfile(self): self.model_sanity(model) # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.get_vector('graph', use_norm=True) sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -181,19 +181,6 @@ def test_persistence_fromfile(self): self.assertTrue(np.allclose(wv.vectors_ngrams, loaded_wv.vectors_ngrams)) self.assertEqual(len(wv.vocab), len(loaded_wv.vocab)) - def test_norm_vectors_not_saved(self): - tmpf = get_tmpfile('gensim_fasttext.tst') - model = FT_gensim(sentences, min_count=1, bucket=BUCKET) - model.init_sims() - model.save(tmpf) - loaded_model = FT_gensim.load(tmpf) - self.assertTrue(loaded_model.wv.vectors_norm is None) - - wv = model.wv - wv.save(tmpf) - loaded_kv = FastTextKeyedVectors.load(tmpf) - self.assertTrue(loaded_kv.vectors_norm is None) - def model_sanity(self, model): self.model_structural_sanity(model) # TODO: add semantic tests, where appropriate diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 7bbbd15aa6..4a399e6ba1 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -345,20 +345,6 @@ def rule(word, count, min_count): model = word2vec.Word2Vec(sentences, min_count=1, trim_rule=rule) self.assertTrue("human" not in model.wv.vocab) - def testVectorsNormNotSaved(self): - """Test vectors_norm isn't saved in model file""" - tmpf = get_tmpfile('gensim_word2vec.tst') - model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() - model.save(tmpf) - loaded_model = word2vec.Word2Vec.load(tmpf) - self.assertTrue(loaded_model.wv.vectors_norm is None) - - wv = model.wv - wv.save(tmpf) - loaded_kv = keyedvectors.KeyedVectors.load(tmpf) - self.assertTrue(loaded_kv.vectors_norm is None) - def obsolete_testLoadPreKeyedVectorModel(self): """Test loading pre-KeyedVectors word2vec model""" @@ -390,13 +376,11 @@ def testPersistenceWord2VecFormat(self): """Test storing/loading the entire model in word2vec format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() model.wv.save_word2vec_format(tmpf, binary=True) binary_model_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) - binary_model_kv.init_sims(replace=False) self.assertTrue(np.allclose(model.wv['human'], binary_model_kv['human'])) norm_only_model = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) - norm_only_model.init_sims(replace=True) + norm_only_model.unit_normalize_all() self.assertFalse(np.allclose(model.wv['human'], norm_only_model['human'])) self.assertTrue(np.allclose(model.wv.vectors_norm[model.wv.vocab['human'].index], norm_only_model['human'])) limited_model_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True, limit=3) @@ -409,7 +393,6 @@ def testPersistenceWord2VecFormat(self): def testNoTrainingCFormat(self): tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() model.wv.save_word2vec_format(tmpf, binary=True) kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) binary_model = word2vec.Word2Vec() @@ -419,7 +402,6 @@ def testNoTrainingCFormat(self): def testTooShortBinaryWord2VecFormat(self): tfile = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() model.wv.save_word2vec_format(tfile, binary=True) f = open(tfile, 'r+b') f.write(b'13') # write wrong (too-long) vector count @@ -429,7 +411,6 @@ def testTooShortBinaryWord2VecFormat(self): def testTooShortTextWord2VecFormat(self): tfile = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() model.wv.save_word2vec_format(tfile, binary=False) f = open(tfile, 'r+b') f.write(b'13') # write wrong (too-long) vector count @@ -440,13 +421,11 @@ def testPersistenceWord2VecFormatNonBinary(self): """Test storing/loading the entire model in word2vec non-binary format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() model.wv.save_word2vec_format(tmpf, binary=False) text_model = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=False) - text_model.init_sims(False) self.assertTrue(np.allclose(model.wv['human'], text_model['human'], atol=1e-6)) norm_only_model = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=False) - norm_only_model.init_sims(True) + norm_only_model.unit_normalize_all() self.assertFalse(np.allclose(model.wv['human'], norm_only_model['human'], atol=1e-6)) self.assertTrue(np.allclose( model.wv.vectors_norm[model.wv.vocab['human'].index], norm_only_model['human'], atol=1e-4 @@ -456,7 +435,6 @@ def testPersistenceWord2VecFormatWithVocab(self): """Test storing/loading the entire model and vocabulary in word2vec format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) binary_model_with_vocab_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) @@ -466,7 +444,6 @@ def testPersistenceKeyedVectorsFormatWithVocab(self): """Test storing/loading the entire model and vocabulary in word2vec format.""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) kv_binary_model_with_vocab = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) @@ -478,7 +455,6 @@ def testPersistenceWord2VecFormatCombinationWithStandardPersistence(self): It was possible prior to 1.0.0 release, now raises Exception""" tmpf = get_tmpfile('gensim_word2vec.tst') model = word2vec.Word2Vec(sentences, min_count=1) - model.wv.init_sims() testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) binary_model_with_vocab_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) @@ -857,7 +833,6 @@ def testPredictOutputWord(self): # when required model parameters have been deleted tmpf = get_tmpfile('gensim_word2vec.tst') - model_with_neg.wv.init_sims() model_with_neg.wv.save_word2vec_format(tmpf, binary=True) kv_model_with_neg = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) binary_model_with_neg = word2vec.Word2Vec() From 1baab2a098c18c120011df66389dab2bb87f5a81 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Wed, 13 May 2020 16:53:25 -0700 Subject: [PATCH 27/60] tighten testParallel --- gensim/test/test_word2vec.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 4a399e6ba1..84a65b9e5b 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -794,15 +794,20 @@ def testSimilarBy(self): def testParallel(self): """Test word2vec parallel training.""" - corpus = utils.RepeatCorpus(LeeCorpus(), 10000) + corpus = utils.RepeatCorpus(LeeCorpus(), 10000) # repeats about 33 times - for workers in [2, 4]: - model = word2vec.Word2Vec(corpus, workers=workers) - sims = model.wv.most_similar('israeli') # noqa:F841 + for workers in [4, ]: # [4, 2] + model = word2vec.Word2Vec(corpus, vector_size=24, min_count=(5 * 33), workers=workers) + origin_word = 'israeli' + expected_neighbor = 'palestinian' + sims = model.wv.most_similar(origin_word, topn=len(model.wv)) # the exact vectors and therefore similarities may differ, due to different thread collisions/randomization # so let's test only for top3 - # TODO: commented out for now; find a more robust way to compare against "gold standard" - # self.assertTrue('palestinian' in [sims[i][0] for i in range(3)]) + from gensim.models.word2vec import FAST_VERSION + print(FAST_VERSION) + print(sims[:20]) + neighbor_rank = [word for word, sim in sims].index(expected_neighbor) + self.assertLess(neighbor_rank, 10) def testRNG(self): """Test word2vec results identical with identical RNG seed.""" @@ -1129,4 +1134,4 @@ def assertLess(self, a, b, msg=None): format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s', level=logging.DEBUG ) - unittest.main() + unittest.main(module='gensim.test.test_word2vec') From 8d2f1fe8c127622ba28f989d4bec716e99fd1de6 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Wed, 13 May 2020 17:49:02 -0700 Subject: [PATCH 28/60] rm .vocab & 'Vocab' classes; add expandable 'vecattrs' --- gensim/models/_fasttext_bin.py | 8 +- gensim/models/doc2vec.py | 43 ++-- gensim/models/doc2vec_inner.pyx | 70 +++--- gensim/models/fasttext.py | 52 ++-- gensim/models/fasttext_inner.pyx | 29 ++- gensim/models/keyedvectors.py | 347 +++++++++++++++++++------- gensim/models/word2vec.py | 316 +++++++---------------- gensim/models/word2vec_corpusfile.pyx | 16 +- gensim/models/word2vec_inner.pxd | 2 +- gensim/models/word2vec_inner.pyx | 73 +++--- gensim/models/wrappers/varembed.py | 17 +- gensim/test/test_doc2vec.py | 35 +-- gensim/test/test_fasttext.py | 122 ++++----- gensim/test/test_keyedvectors.py | 41 ++- gensim/test/test_word2vec.py | 193 +++++++------- 15 files changed, 689 insertions(+), 675 deletions(-) diff --git a/gensim/models/_fasttext_bin.py b/gensim/models/_fasttext_bin.py index 7853a7a8d6..64379a878c 100644 --- a/gensim/models/_fasttext_bin.py +++ b/gensim/models/_fasttext_bin.py @@ -531,9 +531,9 @@ def _dict_save(fout, model, encoding): # In the unsupervised case we have only words (no labels). Hence both fields # are equal. - fout.write(np.int32(len(model.wv.vocab)).tobytes()) + fout.write(np.int32(len(model.wv)).tobytes()) - fout.write(np.int32(len(model.wv.vocab)).tobytes()) + fout.write(np.int32(len(model.wv)).tobytes()) # nlabels=0 <- no labels we are in unsupervised mode fout.write(np.int32(0).tobytes()) @@ -544,7 +544,7 @@ def _dict_save(fout, model, encoding): fout.write(np.int64(-1)) for word in model.wv.index2word: - word_count = model.wv.vocab[word].count + word_count = model.wv.get_vecattr(word, 'count') fout.write(word.encode(encoding)) fout.write(_END_OF_WORD_MARKER) fout.write(np.int64(word_count).tobytes()) @@ -572,7 +572,7 @@ def _input_save(fout, model): ngrams_n, ngrams_dim = model.wv.vectors_ngrams.shape assert vocab_dim == ngrams_dim - assert vocab_n == len(model.wv.vocab) + assert vocab_n == len(model.wv) assert ngrams_n == model.wv.bucket fout.write(struct.pack('@2q', vocab_n + ngrams_n, vocab_dim)) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 32d5ccce06..7ea7b17927 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -83,7 +83,7 @@ from gensim.models import Word2Vec from six.moves import range from six import string_types, integer_types, itervalues -from gensim.models.keyedvectors import KeyedVectors, ConcatList, pseudorandom_weak_vector +from gensim.models.keyedvectors import KeyedVectors, pseudorandom_weak_vector logger = logging.getLogger(__name__) @@ -145,11 +145,12 @@ def __str__(self): @dataclass -class DoctagVocab: +class Doctag: """A dataclass shape-compatible with keyedvectors.SimpleVocab, extended to record details of string document tags discovered during the initial vocabulary scan. - Will not be used if all presented document tags are ints. + Will not be used if all presented document tags are ints. No longer used in a + completed model: just used during initial scan, and for backward compatibility. """ __slots__ = ('doc_count', 'index', 'word_count') doc_count: int # number of docs where tag appeared @@ -165,10 +166,6 @@ def count(self, new_val): self.doc_count = new_val -# compatibility alias, allowing prior namedtuples to unpickle -Doctag = DoctagVocab - - class Doc2Vec(Word2Vec): def __init__(self, documents=None, corpus_file=None, vector_size=100, dm_mean=None, dm=1, dbow_words=0, dm_concat=0, dm_tag_count=1, dv=None, dv_mapfile=None, comment=None, trim_rule=None, callbacks=(), @@ -352,18 +349,26 @@ def reset_weights(self): def reset_from(self, other_model): """Copy shareable data structures from another (possibly pre-trained) model. + This specifically causes some structures to be shared, so is limited to + structures (like those rleated to the known word/tag vocabularies) that + won't change during training or thereafter. Beware vocabulary edits/updates + to either model afterwards: the partial sharing and out-of-band modification + may leave the other model in a broken state. + Parameters ---------- other_model : :class:`~gensim.models.doc2vec.Doc2Vec` Other model whose internal data structures will be copied over to the current object. """ - self.wv.vocab = other_model.wv.vocab - self.wv.index2key = other_model.wv.index2key + self.wv.key_to_index = other_model.wv.key_to_index + self.wv.index_to_key = other_model.wv.index_to_key + self.wv.expandos = other_model.wv.expandos self.cum_table = other_model.cum_table self.corpus_count = other_model.corpus_count - self.dv.vocab = other_model.dv.vocab - self.dv.index2key = other_model.dv.index2key + self.dv.key_to_index = other_model.dv.key_to_index + self.dv.index_to_key = other_model.dv.index_to_key + self.dv.expandos = other_model.dv.expandos self.reset_weights() def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, @@ -583,7 +588,7 @@ def estimated_lookup_memory(self): The estimated RAM required to look up a tag in bytes. """ - return 60 * len(self.dv.vocab) + 140 * len(self.dv.vocab) + return 60 * len(self.dv) + 140 * len(self.dv) def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps=None): """Infer a vector for given post-bulk training document. @@ -666,7 +671,7 @@ def __getitem__(self, tag): """ if isinstance(tag, string_types + integer_types + (integer,)): - if tag not in self.wv.vocab: + if tag not in self.wv: return self.dv[tag] return self.wv[tag] return vstack([self[i] for i in tag]) @@ -748,7 +753,8 @@ def save_word2vec_format(self, fname, doctag_vec=False, word_vec=True, prefix='* append = True self.dv.save_word2vec_format( fname, prefix=prefix, fvocab=fvocab, binary=binary, - write_first_line=write_first_line, append=append) + write_first_line=write_first_line, append=append, + sort_attr='doc_count') def init_sims(self, replace=False): """Pre-compute L2-normalized vectors. @@ -981,10 +987,13 @@ def _scan_vocab(self, corpus_iterable, progress_per, trim_rule): # adjust indexes/list to account for range of pure-int keyed doctags for key in doctags_list: doctags_lookup[key].index = doctags_lookup[key].index + max_rawint + 1 - doctags_list = ConcatList([range(0, max_rawint + 1), doctags_list]) + doctags_list = list(range(0, max_rawint + 1)) + doctags_list - self.dv.map = doctags_lookup - self.dv.index2key = doctags_list + self.dv.index_to_key = doctags_list + for t, dt in doctags_lookup.items(): + self.dv.key_to_index[t] = dt.index + self.dv.set_vecattr(t, 'word_count', dt.word_count) + self.dv.set_vecattr(t, 'doc_count', dt.doc_count) self.raw_vocab = vocab return total_words, corpus_count diff --git a/gensim/models/doc2vec_inner.pyx b/gensim/models/doc2vec_inner.pyx index e06aa00a35..4f58c69d90 100644 --- a/gensim/models/doc2vec_inner.pyx +++ b/gensim/models/doc2vec_inner.pyx @@ -233,28 +233,28 @@ cdef init_d2v_config(Doc2VecConfig *c, model, alpha, learn_doctags, learn_words, c[0].learn_hidden = learn_hidden c[0].alpha = alpha c[0].layer1_size = model.layer1_size - c[0].vector_size = model.docvecs.vector_size + c[0].vector_size = model.dv.vector_size c[0].workers = model.workers c[0].docvecs_count = docvecs_count c[0].window = model.window c[0].expected_doctag_len = model.dm_tag_count - if '\0' in model.wv.vocab: - c[0].null_word_index = model.wv.vocab['\0'].index + if '\0' in model.wv: + c[0].null_word_index = model.wv.get_index('\0') # default vectors, locks from syn0/doctag_syn0 if word_vectors is None: word_vectors = model.wv.vectors c[0].word_vectors = (np.PyArray_DATA(word_vectors)) if doctag_vectors is None: - doctag_vectors = model.docvecs.vectors_docs + doctag_vectors = model.dv.vectors c[0].doctag_vectors = (np.PyArray_DATA(doctag_vectors)) if word_locks is None: word_locks = model.wv.vectors_lockf c[0].word_locks = (np.PyArray_DATA(word_locks)) if doctag_locks is None: - doctag_locks = model.docvecs.vectors_lockf + doctag_locks = model.dv.vectors_lockf c[0].doctag_locks = (np.PyArray_DATA(doctag_locks)) if c[0].hs: @@ -332,22 +332,24 @@ def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=train_words, work=work, neu1=None, word_vectors=word_vectors, word_locks=word_locks, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) - c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) + vocab_sample_ints = model.wv.expandos['sample_int'] + if c.hs: + vocab_codes = model.wv.expandos['code'] + vocab_points = model.wv.expandos['point'] - vlookup = model.wv.vocab i = 0 for token in doc_words: - predict_word = vlookup[token] if token in vlookup else None - if predict_word is None: # shrink document to leave out word + word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + if word_index is None: # shrink document to leave out word continue # leaving i unchanged - if c.sample and predict_word.sample_int < random_int32(&c.next_random): + if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): continue - c.indexes[i] = predict_word.index + c.indexes[i] = word_index if c.hs: - c.codelens[i] = len(predict_word.code) - c.codes[i] = np.PyArray_DATA(predict_word.code) - c.points[i] = np.PyArray_DATA(predict_word.point) + c.codelens[i] = len(vocab_codes[word_index]) + c.codes[i] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[i] = np.PyArray_DATA(vocab_points[word_index]) result += 1 i += 1 if i == MAX_DOCUMENT_LEN: @@ -458,22 +460,24 @@ def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=N init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=False, work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) - c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) + vocab_sample_ints = model.wv.expandos['sample_int'] + if c.hs: + vocab_codes = model.wv.expandos['code'] + vocab_points = model.wv.expandos['point'] - vlookup = model.wv.vocab i = 0 for token in doc_words: - predict_word = vlookup[token] if token in vlookup else None - if predict_word is None: # shrink document to leave out word + word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + if word_index is None: # shrink document to leave out word continue # leaving i unchanged - if c.sample and predict_word.sample_int < random_int32(&c.next_random): + if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): continue - c.indexes[i] = predict_word.index + c.indexes[i] = word_index if c.hs: - c.codelens[i] = len(predict_word.code) - c.codes[i] = np.PyArray_DATA(predict_word.code) - c.points[i] = np.PyArray_DATA(predict_word.point) + c.codelens[i] = len(vocab_codes[word_index]) + c.codes[i] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[i] = np.PyArray_DATA(vocab_points[word_index]) result += 1 i += 1 if i == MAX_DOCUMENT_LEN: @@ -596,25 +600,27 @@ def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=False, work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) - c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) + vocab_sample_ints = model.wv.expandos['sample_int'] + if c.hs: + vocab_codes = model.wv.expandos['code'] + vocab_points = model.wv.expandos['point'] if c.doctag_len != c.expected_doctag_len: return 0 # skip doc without expected number of tags - vlookup = model.wv.vocab i = 0 for token in doc_words: - predict_word = vlookup[token] if token in vlookup else None - if predict_word is None: # shrink document to leave out word + word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + if word_index is None: # shrink document to leave out word continue # leaving i unchanged - if c.sample and predict_word.sample_int < random_int32(&c.next_random): + if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): continue - c.indexes[i] = predict_word.index + c.indexes[i] = word_index if c.hs: - c.codelens[i] = len(predict_word.code) - c.codes[i] = np.PyArray_DATA(predict_word.code) - c.points[i] = np.PyArray_DATA(predict_word.point) + c.codelens[i] = len(vocab_codes[word_index]) + c.codes[i] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[i] = np.PyArray_DATA(vocab_points[word_index]) result += 1 i += 1 if i == MAX_DOCUMENT_LEN: diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 6aebe01e76..8c7f0015f4 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -498,7 +498,7 @@ def prepare_weights(self, update=False): def init_post_load(self, hidden_output): num_vectors = len(self.wv.vectors) - vocab_size = len(self.wv.vocab) + vocab_size = len(self.wv) vector_size = self.wv.vector_size assert num_vectors > 0, 'expected num_vectors to be initialized already' @@ -574,7 +574,7 @@ def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, prog """ if not update: self.wv.init_ngrams_weights(self.seed) - elif not len(self.wv.vocab): + elif not len(self.wv): raise RuntimeError( "You cannot do an online vocabulary-update of a model which has no prior vocabulary. " "First build the vocabulary of your model with a corpus " @@ -582,7 +582,7 @@ def build_vocab(self, corpus_iterable=None, corpus_file=None, update=False, prog "before doing an online update." ) else: - self.old_vocab_len = len(self.wv.vocab) + self.old_vocab_len = len(self.wv) retval = super(FastText, self).build_vocab( corpus_iterable=corpus_iterable, corpus_file=corpus_file, update=update, progress_per=progress_per, @@ -599,24 +599,24 @@ def _clear_post_train(self): self.wv.adjust_vectors() # ensure composite-word vecs reflect latest training def estimate_memory(self, vocab_size=None, report=None): - vocab_size = vocab_size or len(self.wv.vocab) + vocab_size = vocab_size or len(self.wv) vec_size = self.vector_size * np.dtype(np.float32).itemsize l1_size = self.layer1_size * np.dtype(np.float32).itemsize report = report or {} - report['vocab'] = len(self.wv.vocab) * (700 if self.hs else 500) - report['syn0_vocab'] = len(self.wv.vocab) * vec_size + report['vocab'] = len(self.wv) * (700 if self.hs else 500) + report['syn0_vocab'] = len(self.wv) * vec_size num_buckets = self.bucket if self.hs: - report['syn1'] = len(self.wv.vocab) * l1_size + report['syn1'] = len(self.wv) * l1_size if self.negative: - report['syn1neg'] = len(self.wv.vocab) * l1_size - if self.word_ngrams > 0 and self.wv.vocab: + report['syn1neg'] = len(self.wv) * l1_size + if self.word_ngrams > 0 and len(self.wv): num_buckets = num_ngrams = 0 if self.bucket: buckets = set() num_ngrams = 0 - for word in self.wv.vocab: + for word in self.wv.key_to_index: hashes = ft_ngram_hashes( word, self.wv.min_n, @@ -630,7 +630,7 @@ def estimate_memory(self, vocab_size=None, report=None): report['syn0_ngrams'] = num_buckets * vec_size # A tuple (48 bytes) with num_ngrams_word ints (8 bytes) for each word # Only used during training, not stored with the model - report['buckets_word'] = 48 * len(self.wv.vocab) + 8 * num_ngrams # FIXME: this looks confused -gojomo + report['buckets_word'] = 48 * len(self.wv) + 8 * num_ngrams # FIXME: this looks confused -gojomo elif self.word_ngrams > 0: logger.warn( 'subword information is enabled, but no vocabulary could be found, estimated required memory might be ' @@ -639,7 +639,7 @@ def estimate_memory(self, vocab_size=None, report=None): report['total'] = sum(report.values()) logger.info( "estimated required memory for %i words, %i buckets and %i dimensions: %i bytes", - len(self.wv.vocab), num_buckets, self.vector_size, report['total'] + len(self.wv), num_buckets, self.vector_size, report['total'] ) return report @@ -1109,16 +1109,16 @@ def _check_model(m): .format(m.wv.vector_size, m.wv.vectors_ngrams) ) - assert len(m.wv.vocab) == m.nwords, ( + assert len(m.wv) == m.nwords, ( 'mismatch between final vocab size ({} words), ' - 'and expected number of words ({} words)'.format(len(m.wv.vocab), m.nwords) + 'and expected number of words ({} words)'.format(len(m.wv), m.nwords) ) - if len(m.wv.vocab) != m.vocab_size: + if len(m.wv) != m.vocab_size: # expecting to log this warning only for pretrained french vector, wiki.fr logger.warning( "mismatch between final vocab size (%s words), and expected vocab size (%s words)", - len(m.wv.vocab), m.vocab_size + len(m.wv), m.vocab_size ) @@ -1258,7 +1258,7 @@ def __contains__(self, word): >>> from gensim.models import FastText >>> cap_path = datapath("crime-and-punishment.bin") >>> model = FastText.load_fasttext_format(cap_path, full_model=False) - >>> 'steamtrain' in model.wv.vocab # If False, is an OOV term + >>> 'steamtrain' in model.wv.key_to_index # If False, is an OOV term False """ @@ -1307,7 +1307,7 @@ def get_vector(self, word, use_norm=False): If word and all ngrams not in vocabulary. """ - if word in self.vocab: + if word in self.key_to_index: return super(FastTextKeyedVectors, self).get_vector(word, use_norm) elif self.bucket == 0: raise KeyError('cannot calculate vector for OOV word without ngrams') @@ -1355,7 +1355,7 @@ def init_ngrams_weights(self, seed): rand_obj.seed(seed) lo, hi = -1.0 / self.vector_size, 1.0 / self.vector_size - vocab_shape = (len(self.vocab), self.vector_size) + vocab_shape = (len(self), self.vector_size) ngrams_shape = (self.bucket, self.vector_size) self.vectors_vocab = rand_obj.uniform(lo, hi, vocab_shape).astype(REAL) @@ -1390,13 +1390,13 @@ def update_ngrams_weights(self, seed, old_vocab_len): rand_obj = np.random rand_obj.seed(seed) - new_vocab = len(self.vocab) - old_vocab_len + new_vocab = len(self) - old_vocab_len self.vectors_vocab = _pad_random(self.vectors_vocab, new_vocab, rand_obj) def init_post_load(self, fb_vectors): """Perform initialization after loading a native Facebook model. - Expects that the vocabulary (self.vocab) has already been initialized. + Expects that the vocabulary (self.key_to_index) has already been initialized. Parameters ---------- @@ -1409,7 +1409,7 @@ def init_post_load(self, fb_vectors): No longer supported. """ - vocab_words = len(self.vocab) + vocab_words = len(self) assert fb_vectors.shape[0] == vocab_words + self.bucket, 'unexpected number of vectors' assert fb_vectors.shape[1] == self.vector_size, 'unexpected vector dimensionality' @@ -1435,7 +1435,7 @@ def adjust_vectors(self): return self.vectors = self.vectors_vocab[:].copy() - for i, w in enumerate(self.index2key): + for i, w in enumerate(self.index_to_key): ngram_buckets = self.buckets_word[i] for nh in ngram_buckets: self.vectors[i] += self.vectors_ngrams[nh] @@ -1451,12 +1451,12 @@ def recalc_word_ngram_buckets(self): TODO: evaluate if this is even necessary, compared to just recalculating """ if self.bucket == 0: - self.buckets_word = [np.array([], dtype=np.uint32)] * len(self.index2key) + self.buckets_word = [np.array([], dtype=np.uint32)] * len(self.index_to_key) return - self.buckets_word = [None] * len(self.index2key) + self.buckets_word = [None] * len(self.index_to_key) - for i, word in enumerate(self.index2key): + for i, word in enumerate(self.index_to_key): self.buckets_word[i] = np.array( ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash), dtype=np.uint32, diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index ebaa664438..cd19a00428 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -475,7 +475,7 @@ cdef void init_ft_config(FastTextConfig *c, model, alpha, _work, _neu1): c.neu1 = np.PyArray_DATA(_neu1) -cdef object populate_ft_config(FastTextConfig *c, vocab, buckets_word, sentences): +cdef object populate_ft_config(FastTextConfig *c, wv, buckets_word, sentences): """Prepare C structures so we can go "full C" and release the Python GIL. We create indices over the sentences. We also perform some calculations for @@ -487,7 +487,7 @@ cdef object populate_ft_config(FastTextConfig *c, vocab, buckets_word, sentences ---------- c : FastTextConfig* A pointer to the struct that will contain the populated indices. - vocab : dict + wv : FastTextKeyedVectors The vocabulary buckets_word : list A list containing the buckets each word appears in @@ -514,24 +514,29 @@ cdef object populate_ft_config(FastTextConfig *c, vocab, buckets_word, sentences cdef int effective_words = 0 cdef int effective_sentences = 0 c.sentence_idx[0] = 0 # indices of the first sentence always start at 0 + + vocab_sample_ints = wv.expandos['sample_int'] + if c.hs: + vocab_codes = wv.expandos['code'] + vocab_points = wv.expandos['point'] for sent in sentences: if not sent: continue # ignore empty sentences; leave effective_sentences unchanged for token in sent: - word = vocab[token] if token in vocab else None - if word is None: + word_index = wv.key_to_index[token] if wv.has_index_for(token) else None + if word_index is None: continue # leaving `effective_words` unchanged = shortening the sentence = expanding the window - if c.sample and word.sample_int < random_int32(&c.next_random): + if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): continue - c.indexes[effective_words] = word.index + c.indexes[effective_words] = word_index - c.subwords_idx_len[effective_words] = (len(buckets_word[word.index])) - c.subwords_idx[effective_words] = np.PyArray_DATA(buckets_word[word.index]) + c.subwords_idx_len[effective_words] = (len(buckets_word[word_index])) + c.subwords_idx[effective_words] = np.PyArray_DATA(buckets_word[word_index]) if c.hs: - c.codelens[effective_words] = len(word.code) - c.codes[effective_words] = np.PyArray_DATA(word.code) - c.points[effective_words] = np.PyArray_DATA(word.point) + c.codelens[effective_words] = len(vocab_codes[word_index]) + c.codes[effective_words] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[effective_words] = np.PyArray_DATA(vocab_points[word_index]) effective_words += 1 if effective_words == MAX_SENTENCE_LEN: @@ -639,7 +644,7 @@ def train_batch_any(model, sentences, alpha, _work, _neu1): init_ft_config(&c, model, alpha, _work, _neu1) - num_words, num_sentences = populate_ft_config(&c, model.wv.vocab, model.wv.buckets_word, sentences) + num_words, num_sentences = populate_ft_config(&c, model.wv, model.wv.buckets_word, sentences) # precompute "reduced window" offsets in a single randint() call for i, randint in enumerate(model.random.randint(0, c.window, num_words)): diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index ad7038a6c5..e30a7da0f3 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -165,8 +165,7 @@ from itertools import chain import logging -from collections import UserList -from dataclasses import dataclass +from collections import UserList, UserDict from numbers import Integral try: @@ -202,11 +201,15 @@ def __init__(self, vector_size, mapfile_path=None): Used to perform operations on the vectors such as vector lookup, distance, similarity etc. """ + self.vector_size = vector_size self.vectors = zeros((0, vector_size), dtype=REAL) # fka (formerly known as) syn0 self.norms = None - self.map = {} - self.vector_size = vector_size - self.index2key = [] # fka index2entity or index2word + + self.index_to_key = [] # fka index2entity or index2word + self.key_to_index = {} + + self.expandos = {} # dynamically-expandable per-vector named, numpy-typed attributes + self.mapfile_path = mapfile_path def _load_specials(self, *args, **kwargs): @@ -214,9 +217,9 @@ def _load_specials(self, *args, **kwargs): super(KeyedVectors, self)._load_specials(*args, **kwargs) if hasattr(self, 'doctags'): self._upconvert_old_d2vkv() - # fixup rename/consolidation into index2key of older index2word, index2entity - if not hasattr(self, 'index2key'): - self.index2key = self.__dict__.pop('index2word', self.__dict__.pop('index2word', None)) + # fixup rename/consolidation into index_to_key of older index2word, index2entity + if not hasattr(self, 'index_to_key'): + self.index_to_key = self.__dict__.pop('index2word', self.__dict__.pop('index2word', None)) # fixup rename into vectors of older syn0 if not hasattr(self, 'vectors'): self.vectors = self.__dict__.pop('syn0', None) @@ -224,13 +227,66 @@ def _load_specials(self, *args, **kwargs): # ensure at least a 'None' in 'norms' to force recalc if not hasattr(self, 'norms'): self.norms = None + # ensure at least an empty 'expandos' + if not hasattr(self, 'expandos'): + self.expandos = {} # fixup rename of vocab into map - if 'map' not in self.__dict__: - self.map = self.__dict__.pop('vocab', None) + if 'key_to_index' not in self.__dict__: + self._upconvert_old_vocab() + + def _upconvert_old_vocab(self): + """Convert a loaded, prior-version instance that had a 'vocab' dict of data objects""" + old_vocab = self.__dict__.pop('vocab', None) + self.key_to_index = {} + for k in old_vocab.keys(): + old_v = old_vocab[k] + self.key_to_index[k] = old_v.index + for attr in old_v.__dict__.keys(): + self.set_vecattr(old_v.index, attr, old_v.__dict__[attr]) + # special case to enforce required type on `sample_int` + if 'sample_int' in self.expandos: + self.expandos['sample_int'] = self.expandos['sample_int'].astype(np.uint32) + + def allocate_vecattrs(self, attrs=None, types=None): + """Ensure arrays for given per-vector extra-attribute names & types exist, at right size. + + The length of the index_to_key list is canonical 'intended size' of KeyedVectors, + even if other properties (vectors array) hasn't yet been allocated or expanded. + So this allocation targets that size. + """ + # with no arguments, simply adjust sizes of existing + if attrs is None: + attrs = list(self.expandos.keys()) + types = [self.expandos[attr].dtype for attr in attrs] + target_size = len(self.index_to_key) + for attr, t in zip(attrs, types): + if attr not in self.expandos: + self.expandos[attr] = np.zeros(target_size, dtype=t) + continue + prev_expando = self.expandos[attr] + if not np.issubdtype(t, prev_expando.dtype): + raise TypeError("can't allocate {0} for existing {1}".format(t, prev_expando.dtype)) + if len(prev_expando) == target_size: + continue # no resizing necessary + prev_count = len(prev_expando) + self.expandos[attr] = np.zeros(target_size, dtype=prev_expando.dtype) + self.expandos[attr][0:min(prev_count, target_size), ] = \ + prev_expando[0:min(prev_count, target_size), ] + + def set_vecattr(self, key, attr, val): + """ TODO """ + self.allocate_vecattrs(attrs=[attr], types=[type(val)]) + index = self.get_index(key) + self.expandos[attr][index] = val + + def get_vecattr(self, key, attr): + """ TODO """ + index = self.get_index(key) + return self.expandos[attr][index] def resize_vectors(self): - """Make underlying vectors match index2key size.""" - target_count = len(self.index2key) + """Make underlying vectors match index_to_key size.""" + target_count = len(self.index_to_key) prev_count = len(self.vectors) if prev_count == target_count: return () @@ -238,8 +294,9 @@ def resize_vectors(self): if hasattr(self, 'mapfile_path') and self.mapfile_path: self.vectors = np.memmap(self.mapfile_path, shape=(target_count, self.vector_size), mode='w+', dtype=REAL) else: - self.vectors = np.empty((target_count, self.vector_size), dtype=REAL) + self.vectors = np.zeros((target_count, self.vector_size), dtype=REAL) self.vectors[0:min(prev_count, target_count), ] = prev_vectors[0:min(prev_count, target_count), ] + self.allocate_vecattrs() self.norms = None return range(prev_count, target_count) @@ -252,11 +309,11 @@ def randomly_initialize_vectors(self, indexes=None, seed=0): indexes = range(0, len(self.vectors)) for i in indexes: self.vectors[i] = pseudorandom_weak_vector(self.vectors.shape[1], - seed_string=(str(self.index2key[i]) + str(seed))) + seed_string=(str(self.index_to_key[i]) + str(seed))) self.norms = None def __len__(self): - return len(self.index2key) + return len(self.index_to_key) def __getitem__(self, key_or_keys): """Get vector representation of `key_or_keys`. @@ -282,12 +339,12 @@ def get_index(self, key): backing vectors array. """ - if key in self.map: - return self.map[key].index - elif isinstance(key, (integer_types, np.integer)) and key < len(self.vectors): + if key in self.key_to_index: + return self.key_to_index[key] + elif isinstance(key, (integer_types, np.integer)) and key < len(self.index_to_key): return key else: - raise KeyError("Key '%s' not in vocabulary" % key) + raise KeyError("Key '%s' not present" % key) def get_vector(self, key, use_norm=False): """Get the key's vector, as a 1D numpy array. @@ -312,19 +369,20 @@ def get_vector(self, key, use_norm=False): """ index = self.get_index(key) if use_norm: + self.fill_norms() result = self.vectors[index] / self.norms[index] else: result = self.vectors[index] - result.setflags(write=False) + result.setflags(write=False) # disallow direct tampering that would invalidate `norms` etc return result def word_vec(self, *args, **kwargs): """Compatibility alias for get_vector()""" return self.get_vector(*args, **kwargs) - def add(self, keys, weights, replace=False): - """Append keys and theirs vectors in a manual way. + def add(self, keys, weights, extras=None, replace=False): + """Append keys and their vectors in a manual way. If some key is already in the vocabulary, the old vector is kept unless `replace` flag is True. Parameters @@ -343,6 +401,12 @@ def add(self, keys, weights, replace=False): weights = np.array(weights).reshape(1, -1) elif isinstance(weights, list): weights = np.array(weights) + if extras is None: + extras = {} + + # TODO? warn if not matching extras already present? + # initially allocate extras, check type compatibility + self.allocate_vecattrs(extras.keys(), [extras[k].dtype for k in extras.keys()]) in_vocab_mask = np.zeros(len(keys), dtype=np.bool) for idx, key in enumerate(keys): @@ -352,16 +416,20 @@ def add(self, keys, weights, replace=False): # add new entities to the vocab for idx in np.nonzero(~in_vocab_mask)[0]: key = keys[idx] - self.map[key] = SimpleVocab(index=len(self.index2key), count=1) - self.index2key.append(key) + self.key_to_index[key] = len(self.index_to_key) + self.index_to_key.append(key) - # add vectors for new entities + # add vectors, extras for new entities self.vectors = vstack((self.vectors, weights[~in_vocab_mask].astype(self.vectors.dtype))) + for attr, extra in extras: + self.expandos[attr] = np.vstack((self.expandos[attr], extra[~in_vocab_mask])) - # change vectors for in_vocab entities if `replace` flag is specified + # change vectors, extras for in_vocab entities if `replace` flag is specified if replace: - in_vocab_idxs = [self.map[keys[idx]].index for idx in np.nonzero(in_vocab_mask)[0]] + in_vocab_idxs = [self.get_index(keys[idx]) for idx in np.nonzero(in_vocab_mask)[0]] self.vectors[in_vocab_idxs] = weights[in_vocab_mask] + for attr, extra in extras: + self.expandos[attr][in_vocab_idxs] = extra[in_vocab_mask] def __setitem__(self, keys, weights): """Add keys and theirs vectors in a manual way. @@ -406,10 +474,10 @@ def most_similar_to_given(self, key1, keys_list): def closer_than(self, key1, key2): """Get all keys that are closer to `key1` than `key2` is to `key1`.""" all_distances = self.distances(key1) - e1_index = self.vocab[key1].index - e2_index = self.vocab[key2].index + e1_index = self.get_index(key1) + e2_index = self.get_index(key2) closer_node_indices = np.where(all_distances < all_distances[e2_index])[0] - return [self.index2key[index] for index in closer_node_indices if index != e1_index] + return [self.index_to_key[index] for index in closer_node_indices if index != e1_index] @deprecated("Use closer_than instead") def words_closer_than(self, word1, word2): @@ -442,27 +510,68 @@ def vectors_norm(self, _): @property def index2entity(self): - return self.index2key + return self.index_to_key @index2entity.setter def index2entity(self, value): - self.index2key = value + self.index_to_key = value @property def index2word(self): - return self.index2key + return self.index_to_key @index2word.setter def index2word(self, value): - self.index2key = value + self.index_to_key = value +# +# @property +# def vocab(self): +# return self.map +# +# @vocab.setter +# def vocab(self, value): +# self.map = value @property - def vocab(self): - return self.map - - @vocab.setter - def vocab(self, value): - self.map = value + def novlookup(self): + """ pseudodict providing pseudovocab objects """ + class Vocaboid(object): + def __init__(self, kv, index): + self.kv = kv + self.index = index + + def __getattr__(self, attr): + if attr not in self.kv.expandos: + raise AttributeError("Attribute '{0}' not in parent KeyedVectors".format(attr)) + return self.kv.get_vecattr(self.index, attr) + + class VocaboidDict(UserDict): + def __init__(self, kv): + super(VocaboidDict, self).__init__() + self.data = kv + + def __getitem__(self, key): + return Vocaboid(self.data, self.data.get_index(key)) + + def __contains(self, key): + return key in self.data + + return VocaboidDict(self) + + def sort_by_descending_frequency(self): + """Sort the vocabulary so the most frequent words have the lowest indexes.""" + if not len(self): + return # noop if empty + count_sorted_indexes = np.argsort(self.expandos['count'])[::-1] + self.index_to_key = list(np.array(self.index_to_key)[count_sorted_indexes]) + self.allocate_vecattrs() + for k in self.expandos: + self.expandos[k] = self.expandos[k][count_sorted_indexes] + if len(self.vectors): + logger.warning("sorting after vectors allocated expensive & error-prone") + self.vectors = self.vectors[count_sorted_indexes] + for i, word in enumerate(self.index_to_key): + self.key_to_index[word] = i def save(self, *args, **kwargs): """Save KeyedVectors. @@ -568,7 +677,7 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip return dists best = matutils.argsort(dists, topn=topn + len(all_keys), reverse=True) # ignore (don't return) keys from the input - result = [(self.index2key[sim + clip_start], float(dists[sim])) + result = [(self.index_to_key[sim + clip_start], float(dists[sim])) for sim in best if (sim + clip_start) not in all_keys] return result[:topn] @@ -779,8 +888,8 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): positive = [positive] all_words = { - self.vocab[word].index for word in positive + negative - if not isinstance(word, ndarray) and word in self.vocab + self.get_index(word) for word in positive + negative + if not isinstance(word, ndarray) and word in self.key_to_index } positive = [ @@ -805,7 +914,7 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): return dists best = matutils.argsort(dists, topn=topn + len(all_words), reverse=True) # ignore (don't return) words from the input - result = [(self.index2key[sim], float(dists[sim])) for sim in best if sim not in all_words] + result = [(self.index_to_key[sim], float(dists[sim])) for sim in best if sim not in all_words] return result[:topn] def doesnt_match(self, words): @@ -888,7 +997,7 @@ def distances(self, word_or_vector, other_words=()): if not other_words: other_vectors = self.vectors else: - other_indices = [self.vocab[word].index for word in other_words] + other_indices = [self.get_index(word) for word in other_words] other_vectors = self.vectors[other_indices] return 1 - self.cosine_similarities(input_vector, other_vectors) @@ -1013,8 +1122,11 @@ def evaluate_word_analogies(self, analogies, restrict_vocab=300000, case_insensi keys 'correct' and 'incorrect'. """ - ok_vocab = [(w, self.vocab[w]) for w in self.index2key[:restrict_vocab]] - ok_vocab = {w.upper(): v for w, v in reversed(ok_vocab)} if case_insensitive else dict(ok_vocab) + ok_keys = self.index_to_key[:restrict_vocab] + if case_insensitive: + ok_vocab = {k.upper(): self.get_index(k) for k in reversed(ok_keys)} + else: + ok_vocab = {k: self.get_index(k) for k in reversed(ok_keys)} oov = 0 logger.info("Evaluating word analogies for top %i words in the model on %s", restrict_vocab, analogies) sections, section = [], None @@ -1048,14 +1160,14 @@ def evaluate_word_analogies(self, analogies, restrict_vocab=300000, case_insensi else: logger.debug("Skipping line #%i with OOV words: %s", line_no, line.strip()) continue - original_vocab = self.vocab - self.vocab = ok_vocab + original_key_to_index = self.key_to_index + self.key_to_index = 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 + self.key_to_index = original_key_to_index for element in sims: predicted = element[0].upper() if case_insensitive else element[0] if predicted in ok_vocab and predicted not in ignore: @@ -1145,15 +1257,18 @@ def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, The ratio of pairs with unknown words. """ - ok_vocab = [(w, self.vocab[w]) for w in self.index2key[:restrict_vocab]] - ok_vocab = {w.upper(): v for w, v in reversed(ok_vocab)} if case_insensitive else dict(ok_vocab) + ok_keys = self.index_to_key[:restrict_vocab] + if case_insensitive: + ok_vocab = {k.upper(): self.get_index(k) for k in reversed(ok_keys)} + else: + ok_vocab = {k: self.get_index(k) for k in reversed(ok_keys)} similarity_gold = [] similarity_model = [] oov = 0 - original_vocab = self.vocab - self.vocab = ok_vocab + original_key_to_index = self.key_to_index + self.key_to_index = ok_vocab with utils.open(pairs, 'rb') as fin: for line_no, line in enumerate(fin): @@ -1183,7 +1298,7 @@ def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, continue similarity_gold.append(sim) # Similarity from the dataset similarity_model.append(self.similarity(a, b)) # Similarity from the model - self.vocab = original_vocab + self.key_to_index = original_key_to_index spearman = stats.spearmanr(similarity_gold, similarity_model) pearson = stats.pearsonr(similarity_gold, similarity_model) if dummy4unknown: @@ -1263,7 +1378,7 @@ def relative_cosine_similarity(self, wa, wb, topn=10): return rcs def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, write_first_line=True, - prefix='', append=False): + prefix='', append=False, sort_attr='count'): """Store the input-hidden weight matrix in the same format used by the original C word2vec-tool, for compatibility. @@ -1281,23 +1396,23 @@ def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, TODO: doc other params """ if total_vec is None: - total_vec = len(self.index2key) + total_vec = len(self.index_to_key) mode = 'wb' if not append else 'ab' - sorted_vocab_keys = sorted(self.vocab.keys(), key=lambda k: -self.vocab[k].count) + sorted_vocab_keys = sorted(self.key_to_index.keys(), key=lambda k: -self.get_vecattr(k, sort_attr)) if fvocab is not None: logger.info("storing vocabulary in %s", fvocab) with utils.open(fvocab, mode) as vout: for word in sorted_vocab_keys: - vout.write(utils.to_utf8("%s%s %s\n" % (prefix, word, self.vocab[word].count))) + vout.write(utils.to_utf8("%s%s %s\n" % (prefix, word, self.get_vecattr(word, sort_attr)))) logger.info("storing %sx%s projection weights into %s", total_vec, self.vector_size, fname) - assert (len(self.index2key), self.vector_size) == self.vectors.shape + assert (len(self.index_to_key), self.vector_size) == self.vectors.shape # after (possibly-empty) initial range of int-only keys, # store in sorted order: most frequent keys at the top index_id_count = 0 - for i, val in enumerate(self.index2key): + for i, val in enumerate(self.index_to_key): if not (i == val): break index_id_count += 1 @@ -1358,6 +1473,66 @@ def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', cls, fname, fvocab=fvocab, binary=binary, encoding=encoding, unicode_errors=unicode_errors, limit=limit, datatype=datatype) + def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='utf8', unicode_errors='strict'): + """Merge in an input-hidden weight matrix loaded from the original C word2vec-tool format, + where it intersects with the current vocabulary. + + No words are added to the existing vocabulary, but intersecting words adopt the file's weights, and + non-intersecting words are left alone. + + Parameters + ---------- + fname : str + The file path to load the vectors from. + lockf : float, optional + Lock-factor value to be set for any imported word-vectors; the + default value of 0.0 prevents further updating of the vector during subsequent + training. Use 1.0 to allow further training updates of merged vectors. + binary : bool, optional + If True, `fname` is in the binary word2vec C format. + encoding : str, optional + Encoding of `text` for `unicode` function (python2 only). + unicode_errors : str, optional + Error handling behaviour, used as parameter for `unicode` function (python2 only). + + """ + overlap_count = 0 + logger.info("loading projection weights from %s", fname) + with utils.open(fname, 'rb') as fin: + header = utils.to_unicode(fin.readline(), encoding=encoding) + vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format + if not vector_size == self.vector_size: + raise ValueError("incompatible vector size %d in file %s" % (vector_size, fname)) + # TOCONSIDER: maybe mismatched vectors still useful enough to merge (truncating/padding)? + if binary: + binary_len = dtype(REAL).itemsize * vector_size + for _ in range(vocab_size): + # mixed text and binary: read text first, then binary + word = [] + while True: + ch = fin.read(1) + if ch == b' ': + break + if ch != b'\n': # ignore newlines in front of words (some binary files have) + word.append(ch) + word = utils.to_unicode(b''.join(word), encoding=encoding, errors=unicode_errors) + weights = np.fromstring(fin.read(binary_len), dtype=REAL) + if word in self.key_to_index: + overlap_count += 1 + self.vectors[self.get_index(word)] = weights + self.wv.vectors_lockf[self.get_index(word)] = lockf # lock-factor: 0.0=no changes + else: + for line_no, line in enumerate(fin): + parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") + if len(parts) != vector_size + 1: + raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) + word, weights = parts[0], [REAL(x) for x in parts[1:]] + if word in self.key_to_index: + overlap_count += 1 + self.vectors[self.get_index(word)] = weights + self.wv.vectors_lockf[self.get_index(word)] = lockf # lock-factor: 0.0=no changes + logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.vectors.shape, fname) + def get_keras_embedding(self, train_embeddings=False): """Get a Keras 'Embedding' layer with weights set as the Word2Vec model's learned word embeddings. @@ -1397,19 +1572,23 @@ def get_keras_embedding(self, train_embeddings=False): return layer def _upconvert_old_d2vkv(self): - from gensim.models.doc2vec import Doctag self.vocab = self.doctags - for k in self.vocab.keys(): - v = self.vocab[k] - if hasattr(v, 'offset'): - self.vocab[k] = Doctag(v.offset + self.max_rawint + 1, v.word_count, v.doc_count) + self._upconvert_old_vocab() # destroys 'vocab', fills 'key_to_index' & 'extras' + for k in self.key_to_index.keys(): + old_offset = self.get_vecattr(k, 'offset') + true_index = old_offset + self.max_rawint + 1 + self.key_to_index[k] = true_index + del self.expandos['offset'] # no longer needed if(self.max_rawint > -1): - self.index2key = ConcatList([range(0, self.max_rawint + 1), self.offset2doctag]) + self.index_to_key = list(range(0, self.max_rawint + 1)) + self.offset2doctag + else: + self.index_to_key = self.offset2doctag self.vectors = self.vectors_docs del self.doctags del self.vectors_docs del self.count del self.max_rawint + del self.offset2doctag def similarity_unseen_docs(self, *args, **kwargs): raise NotImplementedError("Call similarity_unseen_docs on a Doc2Vec model instead.") @@ -1421,21 +1600,6 @@ def similarity_unseen_docs(self, *args, **kwargs): EuclideanKeyedVectors = KeyedVectors -@dataclass -class SimpleVocab: - """A single vocabulary item, used internally for collecting per-word position in the - backing array (.index), and frequency/sampling info from a corpus survey (.count). - - Using a dataclass with fixed __slots__ saves 200+ bytes per entry over the prior - approach (which used a freely-expandable __dict__) – but now requires specialized - uses to define their own expanded data items, which should always include `count` - and `index` properties. - """ - __slots__ = ('count', 'index') - count: int - index: int - - class CompatVocab(object): def __init__(self, **kwargs): """A single vocabulary item, used internally for collecting per-word frequency/sampling info, @@ -1462,8 +1626,8 @@ def __str__(self): def _add_word_to_result(result, counts, word, weights, vocab_size): - word_id = len(result.vocab) - if word in result.vocab: + word_id = len(result) + if result.has_index_for(word): logger.warning("duplicate word '%s' in word2vec file, ignoring all but first", word) return if counts is None: @@ -1476,16 +1640,17 @@ def _add_word_to_result(result, counts, word, weights, vocab_size): logger.warning("vocabulary file is incomplete: '%s' is missing", word) word_count = None - result.vocab[word] = SimpleVocab(index=word_id, count=word_count) + result.key_to_index[word] = word_id + result.index_to_key.append(word) + result.set_vecattr(word, 'count', word_count) result.vectors[word_id] = weights - result.index2key.append(word) def _add_bytes_to_result(result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors): start = 0 processed_words = 0 bytes_per_vector = vector_size * dtype(REAL).itemsize - max_words = vocab_size - len(result.vocab) + max_words = vocab_size - len(result) for _ in range(max_words): i_space = chunk.find(b' ', start) i_vector = i_space + 1 @@ -1596,13 +1761,13 @@ def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8' vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size) else: _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, encoding) - if result.vectors.shape[0] != len(result.vocab): + if result.vectors.shape[0] != len(result): logger.info( "duplicate words detected, shrinking matrix size from %i to %i", - result.vectors.shape[0], len(result.vocab) + result.vectors.shape[0], len(result) ) - result.vectors = ascontiguousarray(result.vectors[: len(result.vocab)]) - assert (len(result.vocab), vector_size) == result.vectors.shape + result.vectors = ascontiguousarray(result.vectors[: len(result)]) + assert (len(result), vector_size) == result.vectors.shape logger.info("loaded %s matrix from %s", result.vectors.shape, fname) return result diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 9c5b033c7b..98061d39cb 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -125,10 +125,7 @@ import os import heapq from timeit import default_timer -from copy import deepcopy from collections import defaultdict, namedtuple -from dataclasses import dataclass -from typing import List from types import GeneratorType import threading import itertools @@ -142,9 +139,7 @@ except ImportError: from Queue import Queue, Empty -from numpy import exp, dot, zeros, dtype, float32 as REAL,\ - uint32, seterr, array, uint8, vstack, fromstring, sqrt,\ - sum as np_sum, ones, logaddexp +from numpy import float32 as REAL import numpy as np from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc @@ -180,55 +175,6 @@ def train_epoch_cbow(model, corpus_file, offset, _cython_vocab, _cur_epoch, _exp raise RuntimeError("Training with corpus_file argument is not supported") -def score_sg_pair(model, word, word2): - """Score the trained Skip-gram model on a pair of words. - - Parameters - ---------- - model : :class:`~gensim.models.word2vec.Word2Vec` - The trained model. - word : :class:`~gensim.models.keyedvectors.Vocab` - Vocabulary representation of the first word. - word2 : :class:`~gensim.models.keyedvectors.Vocab` - Vocabulary representation of the second word. - - Returns - ------- - float - Logarithm of the sum of exponentiations of input words. - - """ - l1 = model.wv.syn0[word2.index] - l2a = deepcopy(model.syn1[word.point]) # 2d matrix, codelen x layer1_size - sgn = (-1.0) ** word.code # ch function, 0-> 1, 1 -> -1 - lprob = -logaddexp(0, -sgn * dot(l1, l2a.T)) - return sum(lprob) - - -def score_cbow_pair(model, word, l1): - """Score the trained CBOW model on a pair of words. - - Parameters - ---------- - model : :class:`~gensim.models.word2vec.Word2Vec` - The trained model. - word : :class:`~gensim.models.keyedvectors.Vocab` - Vocabulary representation of the first word. - l1 : list of float - Vector representation of the second word. - - Returns - ------- - float - Logarithm of the sum of exponentiations of input words. - - """ - l2a = model.syn1[word.point] # 2d matrix, codelen x layer1_size - sgn = (-1.0) ** word.code # ch function, 0-> 1, 1 -> -1 - lprob = -logaddexp(0, -sgn * dot(l1, l2a.T)) - return sum(lprob) - - class Word2Vec(utils.SaveLoad): def __init__(self, sentences=None, corpus_file=None, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, @@ -328,7 +274,7 @@ def __init__(self, sentences=None, corpus_file=None, vector_size=100, alpha=0.02 * `min_count` (int) - the minimum count threshold. sorted_vocab : {0, 1}, optional If 1, sort the vocabulary by descending frequency before assigning word indexes. - See :meth:`~gensim.models.word2vec.Word2VecVocab.sort_vocab()`. + See :meth:`~gensim.models.keyedvectors.KeyedVectors.sort_by_descending_frequency()`. batch_words : int, optional Target size (in words) for batches of examples passed to worker threads (and thus cython routines).(Larger batches will be passed if individual @@ -573,14 +519,6 @@ def scan_vocab(self, corpus_iterable=None, corpus_file=None, progress_per=10000, return total_words, corpus_count - def sort_vocab(self): - """Sort the vocabulary so the most frequent words have the lowest indexes.""" - if len(self.wv.vectors): - raise RuntimeError("cannot sort vocabulary after model weights already initialized.") - self.wv.index2key.sort(key=lambda word: self.wv.vocab[word].count, reverse=True) - for i, word in enumerate(self.wv.index2key): - self.wv.vocab[word].index = i - def prepare_vocab( self, update=False, keep_raw_vocab=False, trim_rule=None, min_count=None, sample=None, dry_run=False): @@ -623,22 +561,26 @@ def prepare_vocab( retain_total, retain_words = 0, [] # Discard words less-frequent than min_count if not dry_run: - self.wv.index2key = [] + self.wv.index_to_key = [] # make stored settings match these applied settings self.min_count = min_count self.sample = sample - self.wv.vocab = {} + self.wv.key_to_index = {} for word, v in iteritems(self.raw_vocab): if keep_vocab_item(word, v, self.effective_min_count, trim_rule=trim_rule): retain_words.append(word) retain_total += v if not dry_run: - self.wv.vocab[word] = W2VVocab(count=v, index=len(self.wv.index2key)) - self.wv.index2key.append(word) + self.wv.key_to_index[word] = len(self.wv.index_to_key) + self.wv.index_to_key.append(word) else: drop_unique += 1 drop_total += v + if not dry_run: + # now update counts + for word in self.wv.index_to_key: + self.wv.set_vecattr(word, 'count', self.raw_vocab[word]) original_unique_total = len(retain_words) + drop_unique retain_unique_pct = len(retain_words) * 100 / max(original_unique_total, 1) logger.info( @@ -657,20 +599,25 @@ def prepare_vocab( new_words = pre_exist_words = [] for word, v in iteritems(self.raw_vocab): if keep_vocab_item(word, v, self.effective_min_count, trim_rule=trim_rule): - if word in self.wv.vocab: + if self.wv.has_index_for(word): pre_exist_words.append(word) pre_exist_total += v if not dry_run: - self.wv.vocab[word].count += v + pass else: new_words.append(word) new_total += v if not dry_run: - self.wv.vocab[word] = W2VVocab(count=v, index=len(self.wv.index2key)) - self.wv.index2key.append(word) + self.wv.key_to_index[word] = len(self.wv.index_to_key) + self.wv.index_to_key.append(word) else: drop_unique += 1 drop_total += v + if not dry_run: + # now update counts + self.wv.allocate_vecattrs(attrs=['count'], types=[type(0)]) + for word in self.wv.index_to_key: + self.wv.set_vecattr(word, 'count', self.wv.get_vecattr(word, 'count') + self.raw_vocab.get(word, 0)) original_unique_total = len(pre_exist_words) + len(new_words) + drop_unique pre_exist_unique_pct = len(pre_exist_words) * 100 / max(original_unique_total, 1) new_unique_pct = len(new_words) * 100 / max(original_unique_total, 1) @@ -692,12 +639,12 @@ def prepare_vocab( threshold_count = sample * retain_total else: # new shorthand: sample >= 1 means downsample all words with higher count than sample - threshold_count = int(sample * (3 + sqrt(5)) / 2) + threshold_count = int(sample * (3 + np.sqrt(5)) / 2) downsample_total, downsample_unique = 0, 0 for w in retain_words: v = self.raw_vocab[w] - word_probability = (sqrt(v / threshold_count) + 1) * (threshold_count / v) + word_probability = (np.sqrt(v / threshold_count) + 1) * (threshold_count / v) if word_probability < 1.0: downsample_unique += 1 downsample_total += word_probability * v @@ -705,7 +652,7 @@ def prepare_vocab( word_probability = 1.0 downsample_total += v if not dry_run: - self.wv.vocab[w].sample_int = int(round(word_probability * 2**32)) + self.wv.set_vecattr(w, 'sample_int', np.uint32(word_probability * (2**32 - 1))) if not dry_run and not keep_raw_vocab: logger.info("deleting the raw counts dictionary of %i items", len(self.raw_vocab)) @@ -729,7 +676,8 @@ def prepare_vocab( self.add_null_word() if self.sorted_vocab and not update: - self.sort_vocab() + self.wv.sort_by_descending_frequency() + if self.hs: # add info about each word's Huffman encoding self.create_binary_tree() @@ -755,14 +703,14 @@ def estimate_memory(self, vocab_size=None, report=None): A dictionary from string representations of the model's memory consuming members to their size in bytes. """ - vocab_size = vocab_size or len(self.wv.vocab) + vocab_size = vocab_size or len(self.wv) report = report or {} report['vocab'] = vocab_size * (700 if self.hs else 500) - report['vectors'] = vocab_size * self.vector_size * dtype(REAL).itemsize + report['vectors'] = vocab_size * self.vector_size * np.dtype(REAL).itemsize if self.hs: - report['syn1'] = vocab_size * self.layer1_size * dtype(REAL).itemsize + report['syn1'] = vocab_size * self.layer1_size * np.dtype(REAL).itemsize if self.negative: - report['syn1neg'] = vocab_size * self.layer1_size * dtype(REAL).itemsize + report['syn1neg'] = vocab_size * self.layer1_size * np.dtype(REAL).itemsize report['total'] = sum(report.values()) logger.info( "estimated required memory for %i words and %i dimensions: %i bytes", @@ -771,10 +719,10 @@ def estimate_memory(self, vocab_size=None, report=None): return report def add_null_word(self): - word, v = '\0', W2VVocab(count=1, sample_int=0) - v.index = len(self.wv.vocab) - self.wv.index2key.append(word) - self.wv.vocab[word] = v + word = '\0' + self.wv.key_to_index[word] = len(self.wv) + self.wv.index_to_key.append(word) + self.wv.set_vecattr(word, 'count', 1) def create_binary_tree(self): """Create a `binary Huffman tree `_ using stored vocabulary @@ -782,7 +730,7 @@ def create_binary_tree(self): Called internally from :meth:`~gensim.models.word2vec.Word2VecVocab.build_vocab`. """ - _assign_binary_codes(self.wv.vocab) + _assign_binary_codes(self.wv) def make_cum_table(self, domain=2**31 - 1): """Create a cumulative-distribution table using stored vocabulary word counts for @@ -793,15 +741,17 @@ def make_cum_table(self, domain=2**31 - 1): That insertion point is the drawn index, coming up in proportion equal to the increment at that slot. """ - vocab_size = len(self.wv.index2key) - self.cum_table = zeros(vocab_size, dtype=uint32) + vocab_size = len(self.wv.index_to_key) + self.cum_table = np.zeros(vocab_size, dtype=np.uint32) # compute sum of all power (Z in paper) train_words_pow = 0.0 for word_index in range(vocab_size): - train_words_pow += self.wv.vocab[self.wv.index2key[word_index]].count**self.ns_exponent + count = self.wv.get_vecattr(word_index, 'count') + train_words_pow += count**self.ns_exponent cumulative = 0.0 for word_index in range(vocab_size): - cumulative += self.wv.vocab[self.wv.index2key[word_index]].count**self.ns_exponent + count = self.wv.get_vecattr(word_index, 'count') + cumulative += count**self.ns_exponent self.cum_table[word_index] = round(cumulative / train_words_pow * domain) if len(self.cum_table) > 0: assert self.cum_table[-1] == domain @@ -824,11 +774,11 @@ def reset_weights(self): self.wv.resize_vectors() self.wv.randomly_initialize_vectors(seed=self.seed) if self.hs: - self.syn1 = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL) + self.syn1 = np.zeros((len(self.wv), self.layer1_size), dtype=REAL) if self.negative: - self.syn1neg = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL) + self.syn1neg = np.zeros((len(self.wv), self.layer1_size), dtype=REAL) - self.wv.vectors_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning + self.wv.vectors_lockf = np.ones(len(self.wv), dtype=REAL) # zeros suppress learning def update_weights(self): """Copy all the existing weights, and reset the weights for the newly added vocabulary.""" @@ -845,14 +795,14 @@ def update_weights(self): ) if self.hs: - self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=REAL)]) + self.syn1 = np.vstack([self.syn1, np.zeros((gained_vocab, self.layer1_size), dtype=REAL)]) if self.negative: - pad = zeros((gained_vocab, self.layer1_size), dtype=REAL) - self.syn1neg = vstack([self.syn1neg, pad]) + pad = np.zeros((gained_vocab, self.layer1_size), dtype=REAL) + self.syn1neg = np.vstack([self.syn1neg, pad]) self.wv.norms = None # do not suppress learning for already learned words - self.wv.vectors_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning + self.wv.vectors_lockf = np.ones(len(self.wv), dtype=REAL) # zeros suppress learning def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, total_examples=None, total_words=None, **kwargs): @@ -1465,7 +1415,7 @@ def _check_training_sanity(self, epochs=None, total_examples=None, total_words=N if self.alpha > self.min_alpha_yet_reached: logger.warning("Effective 'alpha' higher than previous training cycles") - if not self.wv.vocab: # should be set by `build_vocab` + if not self.wv.key_to_index: # should be set by `build_vocab` raise RuntimeError("you must first build vocabulary before training the model") if not len(self.wv.vectors): raise RuntimeError("you must initialize vectors before training the model") @@ -1488,7 +1438,7 @@ def _check_training_sanity(self, epochs=None, total_examples=None, total_words=N logger.info( "training model with %i workers on %i vocabulary and %i features, " "using sg=%s hs=%s sample=%s negative=%s window=%s", - self.workers, len(self.wv.vocab), self.layer1_size, self.sg, + self.workers, len(self.wv), self.layer1_size, self.sg, self.hs, self.sample, self.negative, self.window ) @@ -1648,11 +1598,11 @@ def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor logger.info( "scoring sentences with %i workers on %i vocabulary and %i features, " "using sg=%s hs=%s sample=%s and negative=%s", - self.workers, len(self.wv.vocab), self.layer1_size, self.sg, self.hs, + self.workers, len(self.wv), self.layer1_size, self.sg, self.hs, self.sample, self.negative ) - if not self.wv.vocab: + if not self.wv.key_to_index: raise RuntimeError("you must first build vocabulary before scoring new data") if not self.hs: @@ -1663,7 +1613,7 @@ def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor def worker_loop(): """Compute log probability for each sentence, lifting lists of sentences from the jobs queue.""" - work = zeros(1, dtype=REAL) # for sg hs, we actually only need one memory loc (running sum) + work = np.zeros(1, dtype=REAL) # for sg hs, we actually only need one memory loc (running sum) neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL) while True: job = job_queue.get() @@ -1742,69 +1692,14 @@ def worker_loop(): ) return sentence_scores[:sentence_count] - def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='utf8', unicode_errors='strict'): - """Merge in an input-hidden weight matrix loaded from the original C word2vec-tool format, - where it intersects with the current vocabulary. - - No words are added to the existing vocabulary, but intersecting words adopt the file's weights, and - non-intersecting words are left alone. - - Parameters - ---------- - fname : str - The file path to load the vectors from. - lockf : float, optional - Lock-factor value to be set for any imported word-vectors; the - default value of 0.0 prevents further updating of the vector during subsequent - training. Use 1.0 to allow further training updates of merged vectors. - binary : bool, optional - If True, `fname` is in the binary word2vec C format. - encoding : str, optional - Encoding of `text` for `unicode` function (python2 only). - unicode_errors : str, optional - Error handling behaviour, used as parameter for `unicode` function (python2 only). - - """ - overlap_count = 0 - logger.info("loading projection weights from %s", fname) - with utils.open(fname, 'rb') as fin: - header = utils.to_unicode(fin.readline(), encoding=encoding) - vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format - if not vector_size == self.wv.vector_size: - raise ValueError("incompatible vector size %d in file %s" % (vector_size, fname)) - # TOCONSIDER: maybe mismatched vectors still useful enough to merge (truncating/padding)? - if binary: - binary_len = dtype(REAL).itemsize * vector_size - for _ in range(vocab_size): - # mixed text and binary: read text first, then binary - word = [] - while True: - ch = fin.read(1) - if ch == b' ': - break - if ch != b'\n': # ignore newlines in front of words (some binary files have) - word.append(ch) - word = utils.to_unicode(b''.join(word), encoding=encoding, errors=unicode_errors) - weights = fromstring(fin.read(binary_len), dtype=REAL) - if word in self.wv.vocab: - overlap_count += 1 - self.wv.vectors[self.wv.vocab[word].index] = weights - self.wv.vectors_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0=no changes - else: - for line_no, line in enumerate(fin): - parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") - if len(parts) != vector_size + 1: - raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) - word, weights = parts[0], [REAL(x) for x in parts[1:]] - if word in self.wv.vocab: - overlap_count += 1 - self.wv.vectors[self.wv.vocab[word].index] = weights - self.wv.vectors_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0=no changes - logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.vectors.shape, fname) - def predict_output_word(self, context_words_list, topn=10): """Get the probability distribution of the center word given context words. + Note this performs a CBOW-style propagation, even in SG models, + and doesn't quite weight the surrounding words the same as in + training -- so it's just one crude way of using a trained model + as a predictor. + Parameters ---------- context_words_list : list of str @@ -1827,23 +1722,21 @@ def predict_output_word(self, context_words_list, topn=10): if not hasattr(self.wv, 'vectors') or not hasattr(self, 'syn1neg'): raise RuntimeError("Parameters required for predicting the output words not found.") - word_vocabs = [self.wv.vocab[w] for w in context_words_list if w in self.wv.vocab] - if not word_vocabs: + word2_indices = [self.wv.get_index(w) for w in context_words_list if w in self.wv] + if not word2_indices: logger.warning("All the input context words are out-of-vocabulary for the current model.") return None - word2_indices = [word.index for word in word_vocabs] - - l1 = np_sum(self.wv.vectors[word2_indices], axis=0) + l1 = np.sum(self.wv.vectors[word2_indices], axis=0) if word2_indices and self.cbow_mean: l1 /= len(word2_indices) # propagate hidden -> output and take softmax to get probabilities - prob_values = exp(dot(l1, self.syn1neg.T)) + prob_values = np.exp(np.dot(l1, self.syn1neg.T)) prob_values /= sum(prob_values) top_indices = matutils.argsort(prob_values, topn=topn, reverse=True) # returning the most probable output words with their probabilities - return [(self.wv.index2key[index1], prob_values[index1]) for index1 in top_indices] + return [(self.wv.index_to_key[index1], prob_values[index1]) for index1 in top_indices] def reset_from(self, other_model): """Borrow shareable pre-built structures from `other_model` and reset hidden layer weights. @@ -1862,8 +1755,10 @@ def reset_from(self, other_model): Another model to copy the internal structures from. """ - self.wv.vocab = other_model.wv.vocab - self.wv.index2key = other_model.wv.index2key + self.wv.key_to_index = other_model.wv.key_to_index + self.wv.index_to_key = other_model.wv.index_to_key + self.wv.expandos = other_model.wv.expandos + self.wv.norms = None self.cum_table = other_model.cum_table self.corpus_count = other_model.corpus_count self.reset_weights() @@ -1879,7 +1774,7 @@ def __str__(self): """ return "%s(vocab=%s, size=%s, alpha=%s)" % ( - self.__class__.__name__, len(self.wv.index2key), self.wv.vector_size, self.alpha + self.__class__.__name__, len(self.wv.index_to_key), self.wv.vector_size, self.alpha ) def save(self, *args, **kwargs): @@ -1943,7 +1838,7 @@ def load(cls, *args, rethrow=False, **kwargs): if not hasattr(model, 'corpus_total_words'): model.corpus_total_words = None if not hasattr(model.wv, 'vectors_lockf') and hasattr(model.wv, 'vectors'): - model.wv.vectors_lockf = getattr(model, 'vectors_lockf', ones(len(model.wv.vectors), dtype=REAL)) + model.wv.vectors_lockf = getattr(model, 'vectors_lockf', np.ones(len(model.wv.vectors), dtype=REAL)) if not hasattr(model, 'random'): model.random = np.random.RandomState(model.seed) if not hasattr(model, 'train_count'): @@ -2134,41 +2029,6 @@ def __iter__(self): i += self.max_sentence_length -@dataclass -class W2VVocab: - """A dataclass shape-compatible with keyedvectors.SimpleVocab, extended with the - `sample_int` property needed by `Word2Vec` models.""" - __slots__ = ('count', 'index', 'sample_int') - count: int - index: int - sample_int: int - - def __init__(self, count=0, index=0, sample_int=2**32): - self.count, self.index, self.sample_int = count, index, sample_int - - def __lt__(self, other): - return self.count < other.count - - -@dataclass -class W2VHSVocab: - """A dataclass shape-compatible with W2VVocab, extended with the `code` and - `point` properties needed by hierarchical-sampling (`hs=1`) `Word2Vec` models.""" - __slots__ = ('count', 'index', 'sample_int', 'code', 'point') - count: int - index: int - sample_int: int - code: List[int] - point: List[int] - - def __init__(self, count=0, index=0, sample_int=2**32, code=None, point=None): - self.count, self.index, self.sample_int, self.code, self.point = \ - count, index, sample_int, code, point - - def __lt__(self, other): - return self.count < other.count - - class Word2VecVocab(utils.SaveLoad): """Obsolete class retained for now as load-compatibility state capture""" pass @@ -2184,43 +2044,34 @@ def __lt__(self, other): return self.count < other.count -def _build_heap(vocab): - heap = list(itervalues(vocab)) +def _build_heap(wv): + heap = list(Heapitem(wv.get_vecattr(i, 'count'), i, None, None) for i in range(len(wv.index_to_key))) heapq.heapify(heap) - for i in range(len(vocab) - 1): + for i in range(len(wv) - 1): min1, min2 = heapq.heappop(heap), heapq.heappop(heap) heapq.heappush( - heap, Heapitem(count=min1.count + min2.count, index=i + len(vocab), left=min1, right=min2) + heap, Heapitem(count=min1.count + min2.count, index=i + len(wv), left=min1, right=min2) ) return heap -def _assign_binary_codes(vocab): +def _assign_binary_codes(wv): """ Appends a binary code to each vocab term. Parameters ---------- - vocab : dict - A dictionary of :class:`gensim.models.word2vec.Vocab` objects. - - Notes - ----- - Expects each term to have an .index attribute that contains the order in - which the term was added to the vocabulary. E.g. term.index == 0 means the - term was added to the vocab first. + wv : KeyedVectors + A collection of word-vectors. Sets the .code and .point attributes of each node. Each code is a numpy.array containing 0s and 1s. Each point is an integer. """ - logger.info("constructing a huffman tree from %i words", len(vocab)) + logger.info("constructing a huffman tree from %i words", len(wv)) - for k in vocab.keys(): - # ensure dataclass items sufficient for huffman-encoding - vocab[k] = W2VHSVocab(vocab[k].count, vocab[k].index, vocab[k].sample_int) - heap = _build_heap(vocab) + heap = _build_heap(wv) if not heap: # # TODO: how can we end up with an empty heap? @@ -2233,15 +2084,18 @@ def _assign_binary_codes(vocab): stack = [(heap[0], [], [])] while stack: node, codes, points = stack.pop() - if node.index < len(vocab): + if node[1] < len(wv): # node[1] = index # leaf node => store its path from the root - node.code, node.point = codes, points + k = node[1] + wv.set_vecattr(k, 'code', codes) + wv.set_vecattr(k, 'point', points) + # node.code, node.point = codes, points max_depth = max(len(codes), max_depth) else: # inner node => continue recursion - points = array(list(points) + [node.index - len(vocab)], dtype=uint32) - stack.append((node.left, array(list(codes) + [0], dtype=uint8), points)) - stack.append((node.right, array(list(codes) + [1], dtype=uint8), points)) + points = np.array(list(points) + [node.index - len(wv)], dtype=np.uint32) + stack.append((node.left, np.array(list(codes) + [0], dtype=np.uint8), points)) + stack.append((node.right, np.array(list(codes) + [1], dtype=np.uint8), points)) logger.info("built huffman tree with maximum node depth %i", max_depth) @@ -2264,7 +2118,7 @@ def _assign_binary_codes(vocab): from gensim.models.word2vec import Word2Vec # noqa:F811 avoid referencing __main__ in pickle - seterr(all='raise') # don't ignore numpy errors + np.seterr(all='raise') # don't ignore numpy errors parser = argparse.ArgumentParser() parser.add_argument("-train", help="Use text data from file TRAIN to train the model", required=True) diff --git a/gensim/models/word2vec_corpusfile.pyx b/gensim/models/word2vec_corpusfile.pyx index 184042250e..e75f250099 100644 --- a/gensim/models/word2vec_corpusfile.pyx +++ b/gensim/models/word2vec_corpusfile.pyx @@ -41,15 +41,19 @@ cdef class CythonVocab: def __init__(self, wv, hs=0, fasttext=0): cdef VocabItem word - for py_token, vocab_item in iteritems(wv.vocab): + vocab_sample_ints = wv.expandos['sample_int'] + if hs: + vocab_codes = wv.expandos['code'] + vocab_points = wv.expandos['point'] + for py_token in wv.key_to_index.keys(): token = any2utf8(py_token) - word.index = vocab_item.index - word.sample_int = vocab_item.sample_int + word.index = wv.get_index(py_token) + word.sample_int = vocab_sample_ints[word.index] if hs: - word.code = np.PyArray_DATA(vocab_item.code) - word.code_len = len(vocab_item.code) - word.point = np.PyArray_DATA(vocab_item.point) + word.code = np.PyArray_DATA(vocab_codes[word.index]) + word.code_len = len(vocab_codes[word.index]) + word.point = np.PyArray_DATA(vocab_points[word.index]) # subwords information, used only in FastText model if fasttext: diff --git a/gensim/models/word2vec_inner.pxd b/gensim/models/word2vec_inner.pxd index fabea96321..67dfbb5770 100644 --- a/gensim/models/word2vec_inner.pxd +++ b/gensim/models/word2vec_inner.pxd @@ -66,7 +66,7 @@ cdef struct Word2VecConfig: REAL_t *syn1 np.uint32_t *points[MAX_SENTENCE_LEN] np.uint8_t *codes[MAX_SENTENCE_LEN] - + # For negative sampling REAL_t *syn1neg np.uint32_t *cum_table diff --git a/gensim/models/word2vec_inner.pyx b/gensim/models/word2vec_inner.pyx index 076ff54b1c..222ca9a254 100755 --- a/gensim/models/word2vec_inner.pyx +++ b/gensim/models/word2vec_inner.pyx @@ -482,7 +482,7 @@ cdef init_w2v_config(Word2VecConfig *c, model, alpha, compute_loss, _work, _neu1 if c[0].hs: c[0].syn1 = (np.PyArray_DATA(model.syn1)) - + if c[0].negative: c[0].syn1neg = (np.PyArray_DATA(model.syn1neg)) c[0].cum_table = (np.PyArray_DATA(model.cum_table)) @@ -528,25 +528,27 @@ def train_batch_sg(model, sentences, alpha, _work, compute_loss): cdef int sent_idx, idx_start, idx_end init_w2v_config(&c, model, alpha, compute_loss, _work) - + vocab_sample_ints = model.wv.expandos['sample_int'] + if c.hs: + vocab_codes = model.wv.expandos['code'] + vocab_points = model.wv.expandos['point'] # prepare C structures so we can go "full C" and release the Python GIL - vlookup = model.wv.vocab c.sentence_idx[0] = 0 # indices of the first sentence always start at 0 for sent in sentences: if not sent: continue # ignore empty sentences; leave effective_sentences unchanged for token in sent: - word = vlookup[token] if token in vlookup else None - if word is None: + if token not in model.wv.key_to_index: continue # leaving `effective_words` unchanged = shortening the sentence = expanding the window - if c.sample and word.sample_int < random_int32(&c.next_random): + word_index = model.wv.key_to_index[token] + if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): continue - c.indexes[effective_words] = word.index + c.indexes[effective_words] = word_index if c.hs: - c.codelens[effective_words] = len(word.code) - c.codes[effective_words] = np.PyArray_DATA(word.code) - c.points[effective_words] = np.PyArray_DATA(word.point) + c.codelens[effective_words] = len(vocab_codes[word_index]) + c.codes[effective_words] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[effective_words] = np.PyArray_DATA(vocab_points[word_index]) effective_words += 1 if effective_words == MAX_SENTENCE_LEN: break # TODO: log warning, tally overflow? @@ -620,24 +622,27 @@ def train_batch_cbow(model, sentences, alpha, _work, _neu1, compute_loss): cdef int sent_idx, idx_start, idx_end init_w2v_config(&c, model, alpha, compute_loss, _work, _neu1) + vocab_sample_ints = model.wv.expandos['sample_int'] + if c.hs: + vocab_codes = model.wv.expandos['code'] + vocab_points = model.wv.expandos['point'] # prepare C structures so we can go "full C" and release the Python GIL - vlookup = model.wv.vocab c.sentence_idx[0] = 0 # indices of the first sentence always start at 0 for sent in sentences: if not sent: continue # ignore empty sentences; leave effective_sentences unchanged for token in sent: - word = vlookup[token] if token in vlookup else None - if word is None: + if token not in model.wv.key_to_index: continue # leaving `effective_words` unchanged = shortening the sentence = expanding the window - if c.sample and word.sample_int < random_int32(&c.next_random): + word_index = model.wv.key_to_index[token] + if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): continue - c.indexes[effective_words] = word.index + c.indexes[effective_words] = word_index if c.hs: - c.codelens[effective_words] = len(word.code) - c.codes[effective_words] = np.PyArray_DATA(word.code) - c.points[effective_words] = np.PyArray_DATA(word.point) + c.codelens[effective_words] = len(vocab_codes[word_index]) + c.codes[effective_words] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[effective_words] = np.PyArray_DATA(vocab_points[word_index]) effective_words += 1 if effective_words == MAX_SENTENCE_LEN: break # TODO: log warning, tally overflow? @@ -714,16 +719,17 @@ def score_sentence_sg(model, sentence, _work): # convert Python structures to primitive types, so we can release the GIL c.work = np.PyArray_DATA(_work) - vlookup = model.wv.vocab + vocab_codes = model.wv.expandos['code'] + vocab_points = model.wv.expandos['point'] i = 0 for token in sentence: - word = vlookup[token] if token in vlookup else None - if word is None: - continue # should drop the - c.indexes[i] = word.index - c.codelens[i] = len(word.code) - c.codes[i] = np.PyArray_DATA(word.code) - c.points[i] = np.PyArray_DATA(word.point) + word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + if word_index is None: + continue # for score, should this be a default negative value? + c.indexes[i] = word_index + c.codelens[i] = len(vocab_codes[word_index]) + c.codes[i] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[i] = np.PyArray_DATA(vocab_points[word_index]) result += 1 i += 1 if i == MAX_SENTENCE_LEN: @@ -810,16 +816,17 @@ def score_sentence_cbow(model, sentence, _work, _neu1): c.work = np.PyArray_DATA(_work) c.neu1 = np.PyArray_DATA(_neu1) - vlookup = model.wv.vocab + vocab_codes = model.wv.expandos['code'] + vocab_points = model.wv.expandos['point'] i = 0 for token in sentence: - word = vlookup[token] if token in vlookup else None - if word is None: + word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + if word_index is None: continue # for score, should this be a default negative value? - c.indexes[i] = word.index - c.codelens[i] = len(word.code) - c.codes[i] = np.PyArray_DATA(word.code) - c.points[i] = np.PyArray_DATA(word.point) + c.indexes[i] = word_index + c.codelens[i] = len(vocab_codes[word_index]) + c.codes[i] = np.PyArray_DATA(vocab_codes[word_index]) + c.points[i] = np.PyArray_DATA(vocab_points[word_index]) result += 1 i += 1 if i == MAX_SENTENCE_LEN: diff --git a/gensim/models/wrappers/varembed.py b/gensim/models/wrappers/varembed.py index 649d608fb3..6f2920c175 100644 --- a/gensim/models/wrappers/varembed.py +++ b/gensim/models/wrappers/varembed.py @@ -18,7 +18,7 @@ import numpy as np from gensim import utils -from gensim.models.keyedvectors import KeyedVectors, SimpleVocab +from gensim.models.keyedvectors import KeyedVectors logger = logging.getLogger(__name__) @@ -87,21 +87,22 @@ def load_word_embeddings(self, word_embeddings, word_to_ix): """ logger.info("Loading the vocabulary") - self.vocab = {} - self.index2word = [] + self.key_to_index = {} + self.index_to_key = [] counts = {} for word in word_to_ix: counts[word] = counts.get(word, 0) + 1 self.vocab_size = len(counts) self.vector_size = word_embeddings.shape[1] self.vectors = np.zeros((self.vocab_size, self.vector_size)) - self.index2word = [None] * self.vocab_size + self.index_to_word = [None] * self.vocab_size logger.info("Corpus has %i words", len(self.vocab)) for word_id, word in enumerate(counts): - self.vocab[word] = SimpleVocab(index=word_id, count=counts[word]) + self.key_to_index[word] = word_id + self.set_extra(word, 'count', counts[word]) self.vectors[word_id] = word_embeddings[word_to_ix[word]] self.index2word[word_id] = word - assert((len(self.vocab), self.vector_size) == self.vectors.shape) + assert((len(self.key_to_index), self.vector_size) == self.vectors.shape) logger.info("Loaded matrix of %d size and %d dimensions", self.vocab_size, self.vector_size) def add_morphemes_to_embeddings(self, morfessor_model, morpho_embeddings, morpho_to_ix): @@ -117,12 +118,12 @@ def add_morphemes_to_embeddings(self, morfessor_model, morpho_embeddings, morpho Mapping morpheme to index. """ - for word in self.vocab: + for word in self.key_to_index: morpheme_embedding = np.array( [ morpho_embeddings[morpho_to_ix.get(m, -1)] for m in morfessor_model.viterbi_segment(word)[0] ] ).sum(axis=0) - self.vectors[self.vocab[word].index] += morpheme_embedding + self.vectors[self.get_index(word)] += morpheme_embedding logger.info("Added morphemes to word vectors") diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index 814ebec002..ccafa8004d 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -90,17 +90,17 @@ def testPersistenceWord2VecFormat(self): test_doc_word = get_tmpfile('gensim_doc2vec.dw') model.save_word2vec_format(test_doc_word, doctag_vec=True, word_vec=True, binary=False) binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_doc_word, binary=False) - self.assertEqual(len(model.wv.vocab) + len(model.dv), len(binary_model_dv.vocab)) + self.assertEqual(len(model.wv) + len(model.dv), len(binary_model_dv)) # test saving document embedding only test_doc = get_tmpfile('gensim_doc2vec.d') model.save_word2vec_format(test_doc, doctag_vec=True, word_vec=False, binary=True) binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_doc, binary=True) - self.assertEqual(len(model.dv), len(binary_model_dv.vocab)) + self.assertEqual(len(model.dv), len(binary_model_dv)) # test saving word embedding only test_word = get_tmpfile('gensim_doc2vec.w') model.save_word2vec_format(test_word, doctag_vec=False, word_vec=True, binary=True) binary_model_dv = keyedvectors.KeyedVectors.load_word2vec_format(test_word, binary=True) - self.assertEqual(len(model.wv.vocab), len(binary_model_dv.vocab)) + self.assertEqual(len(model.wv), len(binary_model_dv)) def obsolete_testLoadOldModel(self): """Test loading an old doc2vec model from indeterminate version""" @@ -108,10 +108,10 @@ def obsolete_testLoadOldModel(self): model_file = 'doc2vec_old' # which version?!? model = doc2vec.Doc2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (3955, 100)) - self.assertTrue(len(model.wv.vocab) == 3955) + self.assertTrue(len(model.wv) == 3955) self.assertTrue(len(model.wv.index2word) == 3955) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.vector_size)) self.assertTrue(model.wv.vectors_lockf.shape == (3955, )) self.assertTrue(model.cum_table.shape == (3955, )) @@ -128,10 +128,10 @@ def obsolete_testLoadOldModelSeparates(self): model_file = 'doc2vec_old_sep' model = doc2vec.Doc2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (3955, 100)) - self.assertTrue(len(model.wv.vocab) == 3955) + self.assertTrue(len(model.wv) == 3955) self.assertTrue(len(model.wv.index2word) == 3955) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.vector_size)) self.assertTrue(model.wv.vectors_lockf.shape == (3955, )) self.assertTrue(model.cum_table.shape == (3955, )) self.assertTrue(model.dv.vectors.shape == (300, 100)) @@ -189,7 +189,7 @@ def _check_old_version(self, old_version): logging.info("TESTING LOAD of %s Doc2Vec MODEL", old_version) saved_models_dir = datapath('old_d2v_models/d2v_{}.mdl') model = doc2vec.Doc2Vec.load(saved_models_dir.format(old_version)) - self.assertTrue(len(model.wv.vocab) == 3) + self.assertTrue(len(model.wv) == 3) self.assertIsNone(model.corpus_total_words) self.assertTrue(model.wv.vectors.shape == (3, 4)) self.assertTrue(model.dv.vectors.shape == (2, 4)) @@ -358,13 +358,13 @@ def test_string_doctags(self): self.assertEqual(model.dv[0].shape, (100,)) self.assertEqual(model.dv['_*0'].shape, (100,)) self.assertTrue(all(model.dv['_*0'] == model.dv[0])) - self.assertTrue(max(d.index for d in model.dv.map.values()) < len(model.dv.index2key)) + self.assertTrue(max(model.dv.key_to_index.values()) < len(model.dv.index_to_key)) self.assertLess( - max(model.dv.get_index(str_key) for str_key in model.dv.map.keys()), + max(model.dv.get_index(str_key) for str_key in model.dv.key_to_index.keys()), len(model.dv.vectors) ) # verify dv.most_similar() returns string doctags rather than indexes - self.assertEqual(model.dv.index2key[0], model.dv.most_similar([model.dv[0]])[0][0]) + self.assertEqual(model.dv.index_to_key[0], model.dv.most_similar([model.dv[0]])[0][0]) def test_empty_errors(self): # no input => "RuntimeError: you must first build vocabulary before training the model" @@ -395,7 +395,9 @@ def model_sanity(self, model, keep_training=True): # inferred vector should be top10 close to bulk-trained one doc0_inferred = model.infer_vector(list(DocsLeeCorpus())[0].words) sims_to_infer = model.dv.most_similar([doc0_inferred], topn=len(model.dv)) - f_rank = [docid for docid, sim in sims_to_infer].index(fire1) + sims_ids = [docid for docid, sim in sims_to_infer] + self.assertTrue(fire1 in sims_ids, "{0} not found in {1}".format(fire1, sims_to_infer)) + f_rank = sims_ids.index(fire1) self.assertLess(f_rank, 10) # fire2 should be top30 close to fire1 @@ -643,20 +645,21 @@ def test_mixed_tag_types(self): mixed_tag_corpus = [doc2vec.TaggedDocument(words, [i, words[0]]) for i, words in enumerate(raw_sentences)] model = doc2vec.Doc2Vec() model.build_vocab(mixed_tag_corpus) - expected_length = len(sentences) + len(model.dv.map) # 9 sentences, 7 unique first tokens + expected_length = len(sentences) + len(model.dv.key_to_index) # 9 sentences, 7 unique first tokens self.assertEqual(len(model.dv.vectors), expected_length) + # TODO: test saving in word2vec format def models_equal(self, model, model2): # check words/hidden-weights - self.assertEqual(len(model.wv.vocab), len(model2.wv.vocab)) + self.assertEqual(len(model.wv), len(model2.wv)) self.assertTrue(np.allclose(model.wv.vectors, model2.wv.vectors)) if model.hs: self.assertTrue(np.allclose(model.syn1, model2.syn1)) if model.negative: self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) # check docvecs - self.assertEqual(len(model.dv.map), len(model2.dv.map)) - self.assertEqual(len(model.dv.index2key), len(model2.dv.index2key)) + self.assertEqual(len(model.dv), len(model2.dv)) + self.assertEqual(len(model.dv.index_to_key), len(model2.dv.index_to_key)) def test_word_vec_non_writeable(self): model = keyedvectors.KeyedVectors.load_word2vec_format(datapath('word2vec_pre_kv_c')) diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index e3a023ee6a..e588422822 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -78,7 +78,7 @@ def test_training(self): sims = model.wv.most_similar('graph', topn=10) self.assertEqual(model.wv.vectors.shape, (12, 12)) - self.assertEqual(len(model.wv.vocab), 12) + self.assertEqual(len(model.wv), 12) self.assertEqual(model.wv.vectors_vocab.shape[1], 12) self.assertEqual(model.wv.vectors_ngrams.shape[1], 12) self.model_sanity(model) @@ -123,7 +123,7 @@ def test_training_fromfile(self): sims = model.wv.most_similar('graph', topn=10) self.assertEqual(model.wv.vectors.shape, (12, 12)) - self.assertEqual(len(model.wv.vocab), 12) + self.assertEqual(len(model.wv), 12) self.assertEqual(model.wv.vectors_vocab.shape[1], 12) self.assertEqual(model.wv.vectors_ngrams.shape[1], 12) self.model_sanity(model) @@ -142,7 +142,7 @@ def test_training_fromfile(self): self.assertEqual(len(oov_vec), 12) def models_equal(self, model, model2): - self.assertEqual(len(model.wv.vocab), len(model2.wv.vocab)) + self.assertEqual(len(model.wv), len(model2.wv)) self.assertEqual(model.wv.bucket, model2.wv.bucket) self.assertTrue(np.allclose(model.wv.vectors_vocab, model2.wv.vectors_vocab)) self.assertTrue(np.allclose(model.wv.vectors_ngrams, model2.wv.vectors_ngrams)) @@ -151,7 +151,7 @@ def models_equal(self, model, model2): self.assertTrue(np.allclose(model.syn1, model2.syn1)) if model.negative: self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) - most_common_word = max(model.wv.vocab.items(), key=lambda item: item[1].count)[0] + most_common_word = max(model.wv.key_to_index, key=lambda word: model.wv.get_vecattr(word, 'count'))[0] self.assertTrue(np.allclose(model.wv[most_common_word], model2.wv[most_common_word])) def test_persistence(self): @@ -164,7 +164,7 @@ def test_persistence(self): wv.save(tmpf) loaded_wv = FastTextKeyedVectors.load(tmpf) self.assertTrue(np.allclose(wv.vectors_ngrams, loaded_wv.vectors_ngrams)) - self.assertEqual(len(wv.vocab), len(loaded_wv.vocab)) + self.assertEqual(len(wv), len(loaded_wv)) def test_persistence_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext1.tst')) as corpus_file: @@ -179,7 +179,7 @@ def test_persistence_fromfile(self): wv.save(tmpf) loaded_wv = FastTextKeyedVectors.load(tmpf) self.assertTrue(np.allclose(wv.vectors_ngrams, loaded_wv.vectors_ngrams)) - self.assertEqual(len(wv.vocab), len(loaded_wv.vocab)) + self.assertEqual(len(wv), len(loaded_wv)) def model_sanity(self, model): self.model_structural_sanity(model) @@ -188,11 +188,11 @@ def model_sanity(self, model): def model_structural_sanity(self, model): """Check a model for basic self-consistency, necessary properties & property correspondences, but no semantic tests.""" - self.assertEqual(model.wv.vectors.shape, (len(model.wv.vocab), model.vector_size)) - self.assertEqual(model.wv.vectors_vocab.shape, (len(model.wv.vocab), model.vector_size)) + self.assertEqual(model.wv.vectors.shape, (len(model.wv), model.vector_size)) + self.assertEqual(model.wv.vectors_vocab.shape, (len(model.wv), model.vector_size)) self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model.vector_size)) self.assertEqual(len(model.wv.vectors_ngrams_lockf), len(model.wv.vectors_ngrams)) - self.assertEqual(len(model.wv.vectors_vocab_lockf), len(model.wv.index2key)) + self.assertEqual(len(model.wv.vectors_vocab_lockf), len(model.wv.index_to_key)) self.assertTrue(np.isfinite(model.wv.vectors_ngrams).all(), "NaN in ngrams") self.assertTrue(np.isfinite(model.wv.vectors_vocab).all(), "NaN in vectors_vocab") if model.negative: @@ -207,7 +207,7 @@ def test_load_fasttext_format(self): self.fail('Unable to load FastText model from file %s: %s' % (self.test_model_file, exc)) vocab_size, model_size = 1762, 10 self.assertEqual(model.wv.vectors.shape, (vocab_size, model_size)) - self.assertEqual(len(model.wv.vocab), vocab_size, model_size) + self.assertEqual(len(model.wv), vocab_size, model_size) self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model_size)) expected_vec = [ @@ -250,7 +250,7 @@ def test_load_fasttext_format(self): self.assertEqual(model.bucket, 1000) self.assertEqual(model.wv.max_n, 6) self.assertEqual(model.wv.min_n, 3) - self.assertEqual(model.wv.vectors.shape, (len(model.wv.vocab), model.vector_size)) + self.assertEqual(model.wv.vectors.shape, (len(model.wv), model.vector_size)) self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model.vector_size)) def test_load_fasttext_new_format(self): @@ -260,7 +260,7 @@ def test_load_fasttext_new_format(self): self.fail('Unable to load FastText model from file %s: %s' % (self.test_new_model_file, exc)) vocab_size, model_size = 1763, 10 self.assertEqual(new_model.wv.vectors.shape, (vocab_size, model_size)) - self.assertEqual(len(new_model.wv.vocab), vocab_size, model_size) + self.assertEqual(len(new_model.wv), vocab_size, model_size) self.assertEqual(new_model.wv.vectors_ngrams.shape, (new_model.wv.bucket, model_size)) expected_vec = [ @@ -303,7 +303,7 @@ def test_load_fasttext_new_format(self): self.assertEqual(new_model.bucket, 1000) self.assertEqual(new_model.wv.max_n, 6) self.assertEqual(new_model.wv.min_n, 3) - self.assertEqual(new_model.wv.vectors.shape, (len(new_model.wv.vocab), new_model.vector_size)) + self.assertEqual(new_model.wv.vectors.shape, (len(new_model.wv), new_model.vector_size)) self.assertEqual(new_model.wv.vectors_ngrams.shape, (new_model.wv.bucket, new_model.vector_size)) def test_load_model_supervised(self): @@ -379,18 +379,19 @@ def test_most_similar_cosmul(self): def test_lookup(self): # In vocab, sanity check - self.assertTrue('night' in self.test_model.wv.vocab) + self.assertTrue('night' in self.test_model.wv.key_to_index) self.assertTrue(np.allclose(self.test_model.wv['night'], self.test_model.wv[['night']])) # Out of vocab check - self.assertFalse('nights' in self.test_model.wv.vocab) + self.assertFalse('nights' in self.test_model.wv.key_to_index) self.assertTrue(np.allclose(self.test_model.wv['nights'], self.test_model.wv[['nights']])) def test_contains(self): # In vocab, sanity check - self.assertTrue('night' in self.test_model.wv.vocab) + self.assertTrue('night' in self.test_model.wv.key_to_index) self.assertTrue('night' in self.test_model.wv) # Out of vocab check - self.assertFalse('nights' in self.test_model.wv.vocab) + self.assertFalse(self.test_model.wv.has_index_for('nights')) + self.assertFalse('nights' in self.test_model.wv.key_to_index) self.assertTrue('nights' in self.test_model.wv) @unittest.skipIf(PYEMD_EXT is False, "pyemd not installed") @@ -675,12 +676,12 @@ def test_sg_neg_training_fromfile(self): def test_online_learning(self): model_hs = FT_gensim(sentences, vector_size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) - self.assertTrue(len(model_hs.wv.vocab), 12) - self.assertTrue(model_hs.wv.vocab['graph'].count, 3) + self.assertEqual(len(model_hs.wv), 12) + self.assertEqual(model_hs.wv.get_vecattr('graph', 'count'), 3) model_hs.build_vocab(new_sentences, update=True) # update vocab - self.assertEqual(len(model_hs.wv.vocab), 14) - self.assertTrue(model_hs.wv.vocab['graph'].count, 4) - self.assertTrue(model_hs.wv.vocab['artificial'].count, 4) + self.assertEqual(len(model_hs.wv), 14) + self.assertEqual(model_hs.wv.get_vecattr('graph', 'count'), 4) + self.assertEqual(model_hs.wv.get_vecattr('artificial', 'count'), 4) def test_online_learning_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext1.tst')) as corpus_file, \ @@ -690,22 +691,22 @@ def test_online_learning_fromfile(self): model_hs = FT_gensim( corpus_file=corpus_file, vector_size=12, min_count=1, seed=42, hs=1, negative=0, bucket=BUCKET) - self.assertTrue(len(model_hs.wv.vocab), 12) - self.assertTrue(model_hs.wv.vocab['graph'].count, 3) + self.assertTrue(len(model_hs.wv), 12) + self.assertTrue(model_hs.wv.get_vecattr('graph', 'count'), 3) model_hs.build_vocab(corpus_file=new_corpus_file, update=True) # update vocab - self.assertEqual(len(model_hs.wv.vocab), 14) - self.assertTrue(model_hs.wv.vocab['graph'].count, 4) - self.assertTrue(model_hs.wv.vocab['artificial'].count, 4) + self.assertEqual(len(model_hs.wv), 14) + self.assertTrue(model_hs.wv.get_vecattr('graph', 'count'), 4) + self.assertTrue(model_hs.wv.get_vecattr('artificial', 'count'), 4) def test_online_learning_after_save(self): tmpf = get_tmpfile('gensim_fasttext.tst') model_neg = FT_gensim(sentences, vector_size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) model_neg.save(tmpf) model_neg = FT_gensim.load(tmpf) - self.assertTrue(len(model_neg.wv.vocab), 12) + self.assertTrue(len(model_neg.wv), 12) model_neg.build_vocab(new_sentences, update=True) # update vocab model_neg.train(new_sentences, total_examples=model_neg.corpus_count, epochs=model_neg.epochs) - self.assertEqual(len(model_neg.wv.vocab), 14) + self.assertEqual(len(model_neg.wv), 14) def test_online_learning_after_save_fromfile(self): with temporary_file(get_tmpfile('gensim_fasttext1.tst')) as corpus_file, \ @@ -718,11 +719,11 @@ def test_online_learning_after_save_fromfile(self): corpus_file=corpus_file, vector_size=12, min_count=0, seed=42, hs=0, negative=5, bucket=BUCKET) model_neg.save(tmpf) model_neg = FT_gensim.load(tmpf) - self.assertTrue(len(model_neg.wv.vocab), 12) + self.assertTrue(len(model_neg.wv), 12) model_neg.build_vocab(corpus_file=new_corpus_file, update=True) # update vocab model_neg.train(corpus_file=new_corpus_file, total_words=model_neg.corpus_total_words, epochs=model_neg.epochs) - self.assertEqual(len(model_neg.wv.vocab), 14) + self.assertEqual(len(model_neg.wv), 14) def online_sanity(self, model): terro, others = [], [] @@ -739,10 +740,10 @@ def online_sanity(self, model): self.assertFalse(np.all(np.equal(start_vecs, model.wv.vectors_vocab))) # checks that `vectors` is different from `vectors_vocab` self.assertFalse(np.all(np.equal(model.wv.vectors, model.wv.vectors_vocab))) - self.assertFalse('terrorism' in model.wv.vocab) + self.assertFalse('terrorism' in model.wv.key_to_index) model.build_vocab(terro, update=True) # update vocab self.assertTrue(model.wv.vectors_ngrams.dtype == 'float32') - self.assertTrue('terrorism' in model.wv.vocab) + self.assertTrue('terrorism' in model.wv.key_to_index) orig0_all = np.copy(model.wv.vectors_ngrams) model.train(terro, total_examples=len(terro), epochs=model.epochs) self.assertFalse(np.allclose(model.wv.vectors_ngrams, orig0_all)) @@ -784,7 +785,7 @@ def test_persistence_word2vec_format(self): model = FT_gensim(sentences, min_count=1, vector_size=12, bucket=BUCKET) model.wv.save_word2vec_format(tmpf, binary=True) loaded_model_kv = KeyedVectors.load_word2vec_format(tmpf, binary=True) - self.assertEqual(len(model.wv.vocab), len(loaded_model_kv.vocab)) + self.assertEqual(len(model.wv), len(loaded_model_kv)) self.assertTrue(np.allclose(model.wv['human'], loaded_model_kv['human'])) def test_bucket_ngrams(self): @@ -812,10 +813,10 @@ def obsolete_testLoadOldModel(self): model_file = 'fasttext_old' model = FT_gensim.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (12, 100)) - self.assertTrue(len(model.wv.vocab) == 12) - self.assertTrue(len(model.wv.index2word) == 12) + self.assertTrue(len(model.wv) == 12) + self.assertTrue(len(model.wv.index_to_key) == 12) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.vector_size)) self.assertTrue(model.wv.vectors_lockf.shape == (12, )) self.assertTrue(model.cum_table.shape == (12, )) @@ -826,10 +827,10 @@ def obsolete_testLoadOldModel(self): model_file = 'fasttext_old_sep' model = FT_gensim.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (12, 100)) - self.assertTrue(len(model.wv.vocab) == 12) - self.assertTrue(len(model.wv.index2word) == 12) + self.assertTrue(len(model.wv) == 12) + self.assertTrue(len(model.wv.index_to_key) == 12) self.assertIsNone(model.corpus_total_words) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.vector_size)) self.assertTrue(model.wv.vectors_lockf.shape == (12, )) self.assertTrue(model.cum_table.shape == (12, )) @@ -872,28 +873,10 @@ def load_vec(fin): def compare_wv(a, b, t): - a_count = {key: value.count for (key, value) in a.vocab.items()} - b_count = {key: value.count for (key, value) in b.vocab.items()} + a_count = {key: a.get_vecattr(key, 'count') for key in a.key_to_index} + b_count = {key: b.get_vecattr(key, 'count') for key in b.key_to_index} t.assertEqual(a_count, b_count) - # - # We don't compare indices because they depend on several things we - # cannot control during testing: - # - # 1. The order in which ties are broken when sorting the vocabulary - # in prepare_vocab - # 2. The order in which vocab terms are added to vocab_raw - # - if False: - a_indices = {key: value.index for (key, value) in a.vocab.items()} - b_indices = {key: value.index for (key, value) in b.vocab.items()} - a_words = [k for k in sorted(a_indices, key=lambda x: a_indices[x])] - b_words = [k for k in sorted(b_indices, key=lambda x: b_indices[x])] - t.assertEqual(a_words, b_words) - - t.assertEqual(a.index2word, b.index2word) - t.assertEqual(a.hash2index, b.hash2index) - # # We do not compare most matrices directly, because they will never # be equal unless many conditions are strictly controlled. @@ -904,11 +887,6 @@ def compare_wv(a, b, t): t.assertEqual(a.vectors_vocab.shape, b.vectors_vocab.shape) # t.assertTrue(np.allclose(a.vectors_vocab, b.vectors_vocab)) - # - # Only if match_gensim=True in init_post_load - # - # t.assertEqual(a.vectors_ngrams.shape, b.vectors_ngrams.shape) - def compare_nn(a, b, t): # @@ -987,6 +965,8 @@ def test_out_of_vocab_gensim(self): """Test whether gensim gives similar results to FB for OOV words. Seems to be broken for our toy model. + + # GM: probably unreasonable to expect identical results given alg randomization & thread jitter """ model = train_gensim() @@ -1112,8 +1092,8 @@ def test_load_native_pretrained(self): def test_load_native_vectors(self): cap_path = datapath("crime-and-punishment.bin") fbkv = gensim.models.fasttext.load_facebook_vectors(cap_path) - self.assertFalse('landlord' in fbkv.vocab) - self.assertTrue('landlady' in fbkv.vocab) + self.assertFalse('landlord' in fbkv.key_to_index) + self.assertTrue('landlady' in fbkv.key_to_index) oov_vector = fbkv['landlord'] iv_vector = fbkv['landlady'] self.assertFalse(np.allclose(oov_vector, iv_vector)) @@ -1592,9 +1572,9 @@ def _check_roundtrip(self, sg): self.assertEqual(model_trained.wv.min_n, model_loaded.wv.min_n) self.assertEqual(model_trained.wv.max_n, model_loaded.wv.max_n) self.assertEqual(model_trained.sample, model_loaded.sample) - self.assertEqual(set(model_trained.wv.index2word), set(model_loaded.wv.index2word)) + self.assertEqual(set(model_trained.wv.index_to_key), set(model_loaded.wv.index_to_key)) - for w in model_trained.wv.index2word: + for w in model_trained.wv.index_to_key: v_orig = model_trained.wv[w] v_loaded = model_loaded.wv[w] self.assertLess(calc_max_diff(v_orig, v_loaded), MAX_WORDVEC_COMPONENT_DIFFERENCE) @@ -1735,9 +1715,9 @@ def _check_load_fasttext_format(self, sg): with temporary_file("load_fasttext.bin") as fpath: model = _create_and_save_fb_model(fpath, model_params) - wv = _read_wordvectors_using_fasttext(fpath, model.wv.index2word) + wv = _read_wordvectors_using_fasttext(fpath, model.wv.index_to_key) - for i, w in enumerate(model.wv.index2word): + for i, w in enumerate(model.wv.index_to_key): diff = calc_max_diff(wv[i, :], model.wv[w]) # Because fasttext command line prints vectors with limited accuracy self.assertLess(diff, 1.0e-4) diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py index 8a0f15b3f0..36f3ac48c5 100644 --- a/gensim/test/test_keyedvectors.py +++ b/gensim/test/test_keyedvectors.py @@ -46,7 +46,7 @@ def test_most_similar_topn(self): self.assertEqual(len(self.vectors.most_similar('war', topn=10)), 10) predicted = self.vectors.most_similar('war', topn=None) - self.assertEqual(len(predicted), len(self.vectors.vocab)) + self.assertEqual(len(predicted), len(self.vectors)) predicted = self.vectors.most_similar('war', topn=0) self.assertEqual(len(predicted), 0) @@ -65,7 +65,7 @@ def test_relative_cosine_similarity(self): ] # synonyms for "good" as per wordnet cos_sim = [] for i in range(len(wordnet_syn)): - if wordnet_syn[i] in self.vectors.vocab: + if wordnet_syn[i] in self.vectors: cos_sim.append(self.vectors.similarity("good", wordnet_syn[i])) cos_sim = sorted(cos_sim, reverse=True) # cosine_similarity of "good" with wordnet_syn in decreasing order # computing relative_cosine_similarity of two similar words @@ -178,9 +178,9 @@ def test_add_multiple(self): vectors = [np.random.randn(self.vectors.vector_size) for _ in range(5)] # Test `add` on already filled kv. - vocab_size = len(self.vectors.vocab) + vocab_size = len(self.vectors) self.vectors.add(entities, vectors, replace=False) - self.assertEqual(vocab_size + len(entities), len(self.vectors.vocab)) + self.assertEqual(vocab_size + len(entities), len(self.vectors)) for ent, vector in zip(entities, vectors): self.assertTrue(np.allclose(self.vectors[ent], vector)) @@ -188,7 +188,7 @@ def test_add_multiple(self): # Test `add` on empty kv. kv = KeyedVectors(self.vectors.vector_size) kv[entities] = vectors - self.assertEqual(len(kv.vocab), len(entities)) + self.assertEqual(len(kv), len(entities)) for ent, vector in zip(entities, vectors): self.assertTrue(np.allclose(kv[ent], vector)) @@ -204,32 +204,32 @@ def test_add_type(self): def test_set_item(self): """Test that __setitem__ works correctly.""" - vocab_size = len(self.vectors.vocab) + vocab_size = len(self.vectors) # Add new entity. entity = '___some_new_entity___' vector = np.random.randn(self.vectors.vector_size) self.vectors[entity] = vector - self.assertEqual(len(self.vectors.vocab), vocab_size + 1) + self.assertEqual(len(self.vectors), vocab_size + 1) self.assertTrue(np.allclose(self.vectors[entity], vector)) # Replace vector for entity in vocab. - vocab_size = len(self.vectors.vocab) + vocab_size = len(self.vectors) vector = np.random.randn(self.vectors.vector_size) self.vectors['war'] = vector - self.assertEqual(len(self.vectors.vocab), vocab_size) + self.assertEqual(len(self.vectors), vocab_size) self.assertTrue(np.allclose(self.vectors['war'], vector)) # __setitem__ on several entities. - vocab_size = len(self.vectors.vocab) + vocab_size = len(self.vectors) entities = ['war', '___some_new_entity1___', '___some_new_entity2___', 'terrorism', 'conflict'] vectors = [np.random.randn(self.vectors.vector_size) for _ in range(len(entities))] self.vectors[entities] = vectors - self.assertEqual(len(self.vectors.vocab), vocab_size + 2) + self.assertEqual(len(self.vectors), vocab_size + 2) for ent, vector in zip(entities, vectors): self.assertTrue(np.allclose(self.vectors[ent], vector)) @@ -243,10 +243,10 @@ def test_load_model_and_vocab_file_replace(self): """Test loading model and voacab files which have decoding errors: replace mode""" model = gensim.models.KeyedVectors.load_word2vec_format( self.model_path, fvocab=self.vocab_path, binary=False, unicode_errors="replace") - self.assertEqual(model.vocab[u'ありがとう�'].count, 123) - self.assertEqual(model.vocab[u'どういたしまして�'].count, 789) - self.assertEqual(model.vocab[u'ありがとう�'].index, 0) - self.assertEqual(model.vocab[u'どういたしまして�'].index, 1) + self.assertEqual(model.get_vecattr(u'ありがとう�', 'count'), 123) + self.assertEqual(model.get_vecattr(u'どういたしまして�', 'count'), 789) + self.assertEqual(model.key_to_index[u'ありがとう�'], 0) + self.assertEqual(model.key_to_index[u'どういたしまして�'], 1) self.assertTrue(np.array_equal( model.get_vector(u'ありがとう�'), np.array([.6, .6, .6], dtype=np.float32))) self.assertTrue(np.array_equal( @@ -256,11 +256,10 @@ def test_load_model_and_vocab_file_ignore(self): """Test loading model and voacab files which have decoding errors: ignore mode""" model = gensim.models.KeyedVectors.load_word2vec_format( self.model_path, fvocab=self.vocab_path, binary=False, unicode_errors="ignore") - print(model.vocab.keys()) - self.assertEqual(model.vocab[u'ありがとう'].count, 123) - self.assertEqual(model.vocab[u'どういたしまして'].count, 789) - self.assertEqual(model.vocab[u'ありがとう'].index, 0) - self.assertEqual(model.vocab[u'どういたしまして'].index, 1) + self.assertEqual(model.get_vecattr(u'ありがとう', 'count'), 123) + self.assertEqual(model.get_vecattr(u'どういたしまして', 'count'), 789) + self.assertEqual(model.key_to_index[u'ありがとう'], 0) + self.assertEqual(model.key_to_index[u'どういたしまして'], 1) self.assertTrue(np.array_equal( model.get_vector(u'ありがとう'), np.array([.6, .6, .6], dtype=np.float32))) self.assertTrue(np.array_equal( @@ -293,7 +292,7 @@ def save_dict_to_word2vec_formated_file(fname, word2vec_dict): class LoadWord2VecFormatTest(unittest.TestCase): def assert_dict_equal_to_model(self, d, m): - self.assertEqual(len(d), len(m.vocab)) + self.assertEqual(len(d), len(m)) for word in d.keys(): self.assertSequenceEqual(list(d[word]), list(m[word])) diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 84a65b9e5b..3e1a1c7935 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -75,45 +75,26 @@ def testBuildVocabFromFreq(self): 'survey': 2, 'user': 3, 'human': 2, 'time': 2, 'interface': 2, 'response': 2 } + freq_dict_orig = freq_dict.copy() model_hs = word2vec.Word2Vec(vector_size=10, min_count=0, seed=42, hs=1, negative=0) model_neg = word2vec.Word2Vec(vector_size=10, min_count=0, seed=42, hs=0, negative=5) model_hs.build_vocab_from_freq(freq_dict) model_neg.build_vocab_from_freq(freq_dict) - self.assertEqual(len(model_hs.wv.vocab), 12) - self.assertEqual(len(model_neg.wv.vocab), 12) - self.assertEqual(model_hs.wv.vocab['minors'].count, 2) - self.assertEqual(model_hs.wv.vocab['graph'].count, 3) - self.assertEqual(model_hs.wv.vocab['system'].count, 4) - self.assertEqual(model_hs.wv.vocab['trees'].count, 3) - self.assertEqual(model_hs.wv.vocab['eps'].count, 2) - self.assertEqual(model_hs.wv.vocab['computer'].count, 2) - self.assertEqual(model_hs.wv.vocab['survey'].count, 2) - self.assertEqual(model_hs.wv.vocab['user'].count, 3) - self.assertEqual(model_hs.wv.vocab['human'].count, 2) - self.assertEqual(model_hs.wv.vocab['time'].count, 2) - self.assertEqual(model_hs.wv.vocab['interface'].count, 2) - self.assertEqual(model_hs.wv.vocab['response'].count, 2) - self.assertEqual(model_neg.wv.vocab['minors'].count, 2) - self.assertEqual(model_neg.wv.vocab['graph'].count, 3) - self.assertEqual(model_neg.wv.vocab['system'].count, 4) - self.assertEqual(model_neg.wv.vocab['trees'].count, 3) - self.assertEqual(model_neg.wv.vocab['eps'].count, 2) - self.assertEqual(model_neg.wv.vocab['computer'].count, 2) - self.assertEqual(model_neg.wv.vocab['survey'].count, 2) - self.assertEqual(model_neg.wv.vocab['user'].count, 3) - self.assertEqual(model_neg.wv.vocab['human'].count, 2) - self.assertEqual(model_neg.wv.vocab['time'].count, 2) - self.assertEqual(model_neg.wv.vocab['interface'].count, 2) - self.assertEqual(model_neg.wv.vocab['response'].count, 2) + self.assertEqual(len(model_hs.wv), 12) + self.assertEqual(len(model_neg.wv), 12) + for k in freq_dict_orig.keys(): + self.assertEqual(model_hs.wv.get_vecattr(k, 'count'), freq_dict_orig[k]) + self.assertEqual(model_neg.wv.get_vecattr(k, 'count'), freq_dict_orig[k]) + new_freq_dict = { 'computer': 1, 'artificial': 4, 'human': 1, 'graph': 1, 'intelligence': 4, 'system': 1, 'trees': 1 } model_hs.build_vocab_from_freq(new_freq_dict, update=True) model_neg.build_vocab_from_freq(new_freq_dict, update=True) - self.assertEqual(model_hs.wv.vocab['graph'].count, 4) - self.assertEqual(model_hs.wv.vocab['artificial'].count, 4) - self.assertEqual(len(model_hs.wv.vocab), 14) - self.assertEqual(len(model_neg.wv.vocab), 14) + self.assertEqual(model_hs.wv.get_vecattr('graph', 'count'), 4) + self.assertEqual(model_hs.wv.get_vecattr('artificial', 'count'), 4) + self.assertEqual(len(model_hs.wv), 14) + self.assertEqual(len(model_neg.wv), 14) def testPruneVocab(self): """Test Prune vocab while scanning sentences""" @@ -124,9 +105,9 @@ def testPruneVocab(self): ["graph", "system"] ] model = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0) - self.assertEqual(len(model.wv.vocab), 2) - self.assertEqual(model.wv.vocab['graph'].count, 3) - self.assertEqual(model.wv.vocab['system'].count, 4) + self.assertEqual(len(model.wv), 2) + self.assertEqual(model.wv.get_vecattr('graph', 'count'), 3) + self.assertEqual(model.wv.get_vecattr('system', 'count'), 4) sentences = [ ["graph", "system"], @@ -136,10 +117,10 @@ def testPruneVocab(self): ["minors", "survey", "minors", "survey", "minors"] ] model = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, max_vocab_size=2, seed=42, hs=1, negative=0) - self.assertEqual(len(model.wv.vocab), 3) - self.assertEqual(model.wv.vocab['graph'].count, 3) - self.assertEqual(model.wv.vocab['minors'].count, 3) - self.assertEqual(model.wv.vocab['system'].count, 4) + self.assertEqual(len(model.wv), 3) + self.assertEqual(model.wv.get_vecattr('graph', 'count'), 3) + self.assertEqual(model.wv.get_vecattr('minors', 'count'), 3) + self.assertEqual(model.wv.get_vecattr('system', 'count'), 4) def testTotalWordCount(self): model = word2vec.Word2Vec(vector_size=10, min_count=0, seed=42) @@ -172,14 +153,14 @@ def testOnlineLearning(self): vocabulary and to a trained model when using a sorted vocabulary""" model_hs = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, seed=42, hs=1, negative=0) model_neg = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, seed=42, hs=0, negative=5) - self.assertTrue(len(model_hs.wv.vocab), 12) - self.assertTrue(model_hs.wv.vocab['graph'].count, 3) + self.assertTrue(len(model_hs.wv), 12) + self.assertTrue(model_hs.wv.get_vecattr('graph', 'count'), 3) model_hs.build_vocab(new_sentences, update=True) model_neg.build_vocab(new_sentences, update=True) - self.assertTrue(model_hs.wv.vocab['graph'].count, 4) - self.assertTrue(model_hs.wv.vocab['artificial'].count, 4) - self.assertEqual(len(model_hs.wv.vocab), 14) - self.assertEqual(len(model_neg.wv.vocab), 14) + self.assertTrue(model_hs.wv.get_vecattr('graph', 'count'), 4) + self.assertTrue(model_hs.wv.get_vecattr('artificial', 'count'), 4) + self.assertEqual(len(model_hs.wv), 14) + self.assertEqual(len(model_neg.wv), 14) def testOnlineLearningAfterSave(self): """Test that the algorithm is able to add new words to the @@ -188,10 +169,10 @@ def testOnlineLearningAfterSave(self): model_neg = word2vec.Word2Vec(sentences, vector_size=10, min_count=0, seed=42, hs=0, negative=5) model_neg.save(tmpf) model_neg = word2vec.Word2Vec.load(tmpf) - self.assertTrue(len(model_neg.wv.vocab), 12) + self.assertTrue(len(model_neg.wv), 12) model_neg.build_vocab(new_sentences, update=True) model_neg.train(new_sentences, total_examples=model_neg.corpus_count, epochs=model_neg.epochs) - self.assertEqual(len(model_neg.wv.vocab), 14) + self.assertEqual(len(model_neg.wv), 14) @unittest.skipIf(os.name == 'nt' and six.PY2, "CythonLineSentence is not supported on Windows + Py27") def testOnlineLearningFromFile(self): @@ -206,18 +187,18 @@ def testOnlineLearningFromFile(self): hs=1, negative=0) model_neg = word2vec.Word2Vec(corpus_file=corpus_file, vector_size=10, min_count=0, seed=42, hs=0, negative=5) - self.assertTrue(len(model_hs.wv.vocab), 12) - self.assertTrue(model_hs.wv.vocab['graph'].count, 3) + self.assertTrue(len(model_hs.wv), 12) + self.assertTrue(model_hs.wv.get_vecattr('graph', 'count'), 3) model_hs.build_vocab(corpus_file=new_corpus_file, update=True) model_hs.train(corpus_file=new_corpus_file, total_words=model_hs.corpus_total_words, epochs=model_hs.epochs) model_neg.build_vocab(corpus_file=new_corpus_file, update=True) model_neg.train( corpus_file=new_corpus_file, total_words=model_hs.corpus_total_words, epochs=model_hs.epochs) - self.assertTrue(model_hs.wv.vocab['graph'].count, 4) - self.assertTrue(model_hs.wv.vocab['artificial'].count, 4) - self.assertEqual(len(model_hs.wv.vocab), 14) - self.assertEqual(len(model_neg.wv.vocab), 14) + self.assertTrue(model_hs.wv.get_vecattr('graph', 'count'), 4) + self.assertTrue(model_hs.wv.get_vecattr('artificial', 'count'), 4) + self.assertEqual(len(model_hs.wv), 14) + self.assertEqual(len(model_neg.wv), 14) @unittest.skipIf(os.name == 'nt' and six.PY2, "CythonLineSentence is not supported on Windows + Py27") def testOnlineLearningAfterSaveFromFile(self): @@ -233,14 +214,14 @@ def testOnlineLearningAfterSaveFromFile(self): hs=0, negative=5) model_neg.save(tmpf) model_neg = word2vec.Word2Vec.load(tmpf) - self.assertTrue(len(model_neg.wv.vocab), 12) + self.assertTrue(len(model_neg.wv), 12) # Check that training works on the same data after load without calling build_vocab model_neg.train(corpus_file=corpus_file, total_words=model_neg.corpus_total_words, epochs=model_neg.epochs) # Train on new corpus file model_neg.build_vocab(corpus_file=new_corpus_file, update=True) model_neg.train(corpus_file=new_corpus_file, total_words=model_neg.corpus_total_words, epochs=model_neg.epochs) - self.assertEqual(len(model_neg.wv.vocab), 14) + self.assertEqual(len(model_neg.wv), 14) def onlineSanity(self, model, trained_model=False): terro, others = [], [] @@ -252,9 +233,9 @@ def onlineSanity(self, model, trained_model=False): self.assertTrue(all('terrorism' not in line for line in others)) model.build_vocab(others, update=trained_model) model.train(others, total_examples=model.corpus_count, epochs=model.epochs) - self.assertFalse('terrorism' in model.wv.vocab) + self.assertFalse('terrorism' in model.wv) model.build_vocab(terro, update=True) - self.assertTrue('terrorism' in model.wv.vocab) + self.assertTrue('terrorism' in model.wv) orig0 = np.copy(model.wv.vectors) model.train(terro, total_examples=len(terro), epochs=model.epochs) self.assertFalse(np.allclose(model.wv.vectors, orig0)) @@ -298,7 +279,7 @@ def testPersistence(self): wv.save(tmpf) loaded_wv = keyedvectors.KeyedVectors.load(tmpf) self.assertTrue(np.allclose(wv.vectors, loaded_wv.vectors)) - self.assertEqual(len(wv.vocab), len(loaded_wv.vocab)) + self.assertEqual(len(wv), len(loaded_wv)) @unittest.skipIf(os.name == 'nt' and six.PY2, "CythonLineSentence is not supported on Windows + Py27") def testPersistenceFromFile(self): @@ -315,7 +296,7 @@ def testPersistenceFromFile(self): wv.save(tmpf) loaded_wv = keyedvectors.KeyedVectors.load(tmpf) self.assertTrue(np.allclose(wv.vectors, loaded_wv.vectors)) - self.assertEqual(len(wv.vocab), len(loaded_wv.vocab)) + self.assertEqual(len(wv), len(loaded_wv)) def testPersistenceWithConstructorRule(self): """Test storing/loading the entire model with a vocab trimming rule passed in the constructor.""" @@ -327,15 +308,15 @@ def testPersistenceWithConstructorRule(self): def testRuleWithMinCount(self): """Test that returning RULE_DEFAULT from trim_rule triggers min_count.""" model = word2vec.Word2Vec(sentences + [["occurs_only_once"]], min_count=2, trim_rule=_rule) - self.assertTrue("human" not in model.wv.vocab) - self.assertTrue("occurs_only_once" not in model.wv.vocab) - self.assertTrue("interface" in model.wv.vocab) + self.assertTrue("human" not in model.wv) + self.assertTrue("occurs_only_once" not in model.wv) + self.assertTrue("interface" in model.wv) def testRule(self): """Test applying vocab trim_rule to build_vocab instead of constructor.""" model = word2vec.Word2Vec(min_count=1) model.build_vocab(sentences, trim_rule=_rule) - self.assertTrue("human" not in model.wv.vocab) + self.assertTrue("human" not in model.wv) def testLambdaRule(self): """Test that lambda trim_rule works.""" @@ -343,7 +324,7 @@ def rule(word, count, min_count): return utils.RULE_DISCARD if word == "human" else utils.RULE_DEFAULT model = word2vec.Word2Vec(sentences, min_count=1, trim_rule=rule) - self.assertTrue("human" not in model.wv.vocab) + self.assertTrue("human" not in model.wv) def obsolete_testLoadPreKeyedVectorModel(self): """Test loading pre-KeyedVectors word2vec model""" @@ -358,19 +339,19 @@ def obsolete_testLoadPreKeyedVectorModel(self): # Model stored in one file model_file = 'word2vec_pre_kv%s' % model_file_suffix model = word2vec.Word2Vec.load(datapath(model_file)) - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.vector_size)) # Model stored in multiple files model_file = 'word2vec_pre_kv_sep%s' % model_file_suffix model = word2vec.Word2Vec.load(datapath(model_file)) - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), model.vector_size)) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.vector_size)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), model.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.vector_size)) def testLoadPreKeyedVectorModelCFormat(self): """Test loading pre-KeyedVectors word2vec model saved in word2vec format""" model = keyedvectors.KeyedVectors.load_word2vec_format(datapath('word2vec_pre_kv_c')) - self.assertTrue(model.vectors.shape[0] == len(model.vocab)) + self.assertTrue(model.vectors.shape[0] == len(model)) def testPersistenceWord2VecFormat(self): """Test storing/loading the entire model in word2vec format.""" @@ -382,7 +363,7 @@ def testPersistenceWord2VecFormat(self): norm_only_model = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True) norm_only_model.unit_normalize_all() self.assertFalse(np.allclose(model.wv['human'], norm_only_model['human'])) - self.assertTrue(np.allclose(model.wv.vectors_norm[model.wv.vocab['human'].index], norm_only_model['human'])) + self.assertTrue(np.allclose(model.wv.get_vector('human', use_norm=True), norm_only_model['human'])) limited_model_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, binary=True, limit=3) self.assertEqual(len(limited_model_kv.vectors), 3) half_precision_model_kv = keyedvectors.KeyedVectors.load_word2vec_format( @@ -428,7 +409,7 @@ def testPersistenceWord2VecFormatNonBinary(self): norm_only_model.unit_normalize_all() self.assertFalse(np.allclose(model.wv['human'], norm_only_model['human'], atol=1e-6)) self.assertTrue(np.allclose( - model.wv.vectors_norm[model.wv.vocab['human'].index], norm_only_model['human'], atol=1e-4 + model.wv.get_vector('human', use_norm=True), norm_only_model['human'], atol=1e-4 )) def testPersistenceWord2VecFormatWithVocab(self): @@ -438,7 +419,8 @@ def testPersistenceWord2VecFormatWithVocab(self): testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) binary_model_with_vocab_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) - self.assertEqual(model.wv.vocab['human'].count, binary_model_with_vocab_kv.vocab['human'].count) + self.assertEqual(model.wv.get_vecattr('human', 'count'), + binary_model_with_vocab_kv.get_vecattr('human', 'count')) def testPersistenceKeyedVectorsFormatWithVocab(self): """Test storing/loading the entire model and vocabulary in word2vec format.""" @@ -447,7 +429,8 @@ def testPersistenceKeyedVectorsFormatWithVocab(self): testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) kv_binary_model_with_vocab = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) - self.assertEqual(model.wv.vocab['human'].count, kv_binary_model_with_vocab.vocab['human'].count) + self.assertEqual(model.wv.get_vecattr('human', 'count'), + kv_binary_model_with_vocab.get_vecattr('human', 'count')) def testPersistenceWord2VecFormatCombinationWithStandardPersistence(self): """Test storing/loading the entire model and vocabulary in word2vec format chained with @@ -458,8 +441,6 @@ def testPersistenceWord2VecFormatCombinationWithStandardPersistence(self): testvocab = get_tmpfile('gensim_word2vec.vocab') model.wv.save_word2vec_format(tmpf, testvocab, binary=True) binary_model_with_vocab_kv = keyedvectors.KeyedVectors.load_word2vec_format(tmpf, testvocab, binary=True) - print("BIN") - print(binary_model_with_vocab_kv) binary_model_with_vocab_kv.save(tmpf) self.assertRaises(AttributeError, word2vec.Word2Vec.load, tmpf) @@ -483,18 +464,18 @@ def testVocab(self): # try vocab building explicitly, using all words model = word2vec.Word2Vec(min_count=1, hs=1, negative=0) model.build_vocab(corpus) - self.assertTrue(len(model.wv.vocab) == 6981) + self.assertTrue(len(model.wv) == 6981) # with min_count=1, we're not throwing away anything, # so make sure the word counts add up to be the entire corpus - self.assertEqual(sum(v.count for v in model.wv.vocab.values()), total_words) + self.assertEqual(sum(model.wv.get_vecattr(k, 'count') for k in model.wv.key_to_index), total_words) # make sure the binary codes are correct - np.allclose(model.wv.vocab['the'].code, [1, 1, 0, 0]) + np.allclose(model.wv.get_vecattr('the', 'code'), [1, 1, 0, 0]) # test building vocab with default params model = word2vec.Word2Vec(hs=1, negative=0) model.build_vocab(corpus) - self.assertTrue(len(model.wv.vocab) == 1750) - np.allclose(model.wv.vocab['the'].code, [1, 1, 1, 0]) + self.assertTrue(len(model.wv) == 1750) + np.allclose(model.wv.get_vecattr('the', 'code'), [1, 1, 1, 0]) # no input => "RuntimeError: you must first build vocabulary before training the model" self.assertRaises(RuntimeError, word2vec.Word2Vec, []) @@ -508,15 +489,15 @@ def testTraining(self): model = word2vec.Word2Vec(vector_size=2, min_count=1, hs=1, negative=0) model.build_vocab(sentences) - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.vectors_norm[model.wv.get_index('graph')] sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -535,15 +516,15 @@ def testTrainingFromFile(self): model = word2vec.Word2Vec(vector_size=2, min_count=1, hs=1, negative=0) model.build_vocab(corpus_file=tf) - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv), 2)) model.train(corpus_file=tf, total_words=model.corpus_total_words, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.vectors_norm[model.wv.get_index('graph')] sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -661,7 +642,7 @@ def test_cbow_hs(self): """Test CBOW w/ hierarchical softmax""" model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=8, hs=1, negative=0, - min_count=5, epochs=10, workers=2, batch_words=1000 + min_count=5, epochs=20, workers=2, batch_words=1000 ) self.model_sanity(model) @@ -669,7 +650,7 @@ def test_cbow_hs(self): def test_cbow_hs_fromfile(self): model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=8, hs=1, negative=0, - min_count=5, epochs=10, workers=2, batch_words=1000 + min_count=5, epochs=20, workers=2, batch_words=1000 ) self.model_sanity(model, with_corpus_file=True) @@ -695,7 +676,7 @@ def test_cosmul(self): # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.vectors_norm[model.wv.get_index('graph')] sims2 = model.wv.most_similar_cosmul(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -706,15 +687,15 @@ def testTrainingCbow(self): # build vocabulary, don't train yet model = word2vec.Word2Vec(vector_size=2, min_count=1, sg=0, hs=1, negative=0) model.build_vocab(sentences) - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), 2)) + self.assertTrue(model.syn1.shape == (len(model.wv), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.vectors_norm[model.wv.get_index('graph')] sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -729,15 +710,15 @@ def testTrainingSgNegative(self): # build vocabulary, don't train yet model = word2vec.Word2Vec(vector_size=2, min_count=1, sg=1, hs=0, negative=2) model.build_vocab(sentences) - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), 2)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.vectors_norm[model.wv.get_index('graph')] sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -752,15 +733,15 @@ def testTrainingCbowNegative(self): # build vocabulary, don't train yet model = word2vec.Word2Vec(vector_size=2, min_count=1, sg=0, hs=0, negative=2) model.build_vocab(sentences) - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 2)) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), 2)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), 2)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), 2)) model.train(sentences, total_examples=model.corpus_count, epochs=model.epochs) sims = model.wv.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.vectors_norm[model.wv.get_index('graph')] sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -797,7 +778,7 @@ def testParallel(self): corpus = utils.RepeatCorpus(LeeCorpus(), 10000) # repeats about 33 times for workers in [4, ]: # [4, 2] - model = word2vec.Word2Vec(corpus, vector_size=24, min_count=(5 * 33), workers=workers) + model = word2vec.Word2Vec(corpus, vector_size=12, min_count=(5 * 33), workers=workers) origin_word = 'israeli' expected_neighbor = 'palestinian' sims = model.wv.most_similar(origin_word, topn=len(model.wv)) @@ -816,13 +797,14 @@ def testRNG(self): self.models_equal(model, model2) def models_equal(self, model, model2): - self.assertEqual(len(model.wv.vocab), len(model2.wv.vocab)) + self.assertEqual(len(model.wv), len(model2.wv)) self.assertTrue(np.allclose(model.wv.vectors, model2.wv.vectors)) if model.hs: self.assertTrue(np.allclose(model.syn1, model2.syn1)) if model.negative: self.assertTrue(np.allclose(model.syn1neg, model2.syn1neg)) - most_common_word = max(model.wv.vocab.items(), key=lambda item: item[1].count)[0] + most_common_word_index = np.argsort(model.wv.expandos['count'])[-1] + most_common_word = model.wv.index_to_key[most_common_word_index] self.assertTrue(np.allclose(model.wv[most_common_word], model2.wv[most_common_word])) def testPredictOutputWord(self): @@ -854,9 +836,9 @@ def testLoadOldModel(self): model_file = 'word2vec_old' # which version?!? model = word2vec.Word2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (12, 100)) - self.assertTrue(len(model.wv.vocab) == 12) + self.assertTrue(len(model.wv) == 12) self.assertTrue(len(model.wv.index2word) == 12) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.wv.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.wv.vector_size)) self.assertTrue(model.wv.vectors_lockf.shape == (12,)) self.assertTrue(model.cum_table.shape == (12,)) @@ -869,9 +851,9 @@ def testLoadOldModelSeparates(self): model_file = 'word2vec_old_sep' model = word2vec.Word2Vec.load(datapath(model_file)) self.assertTrue(model.wv.vectors.shape == (12, 100)) - self.assertTrue(len(model.wv.vocab) == 12) + self.assertTrue(len(model.wv) == 12) self.assertTrue(len(model.wv.index2word) == 12) - self.assertTrue(model.syn1neg.shape == (len(model.wv.vocab), model.wv.vector_size)) + self.assertTrue(model.syn1neg.shape == (len(model.wv), model.wv.vector_size)) self.assertTrue(model.wv.vectors_lockf.shape == (12,)) self.assertTrue(model.cum_table.shape == (12,)) @@ -933,7 +915,7 @@ def _check_old_version(self, old_version): saved_models_dir = datapath('old_w2v_models/w2v_{}.mdl') model = word2vec.Word2Vec.load(saved_models_dir.format(old_version)) self.assertIsNone(model.corpus_total_words) - self.assertTrue(len(model.wv.vocab) == 3) + self.assertTrue(len(model.wv) == 3) try: self.assertTrue(model.wv.vectors.shape == (3, 4)) except AttributeError as ae: @@ -1007,9 +989,8 @@ def test_reset_from(self): """Test if reset_from() uses pre-built structures from other model""" model = word2vec.Word2Vec(sentences, min_count=1) other_model = word2vec.Word2Vec(new_sentences, min_count=1) - other_vocab = other_model.wv.vocab model.reset_from(other_model) - self.assertEqual(model.wv.vocab, other_vocab) + self.assertEqual(model.wv.key_to_index, other_model.wv.key_to_index) def test_compute_training_loss(self): model = word2vec.Word2Vec(min_count=1, sg=1, negative=5, hs=1) From fc65525f61469b386679fa26e16735ff88c72b7b Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Fri, 15 May 2020 10:22:16 -0700 Subject: [PATCH 29/60] update usages (no vocabs) --- gensim/models/keyedvectors.py | 30 ++++++---- gensim/models/poincare.py | 74 ++++++++++++------------- gensim/models/translation_matrix.py | 6 +- gensim/models/wrappers/varembed.py | 10 ++-- gensim/similarities/nmslib.py | 2 +- gensim/similarities/termsim.py | 2 +- gensim/test/test_keras_integration.py | 10 ++-- gensim/test/test_poincare.py | 30 +++++----- gensim/test/test_similarities.py | 6 +- gensim/test/test_varembed_wrapper.py | 2 +- gensim/test/test_wordrank_wrapper.py | 6 +- gensim/topic_coherence/text_analysis.py | 6 +- 12 files changed, 94 insertions(+), 90 deletions(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index e30a7da0f3..611accdaa8 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -523,18 +523,26 @@ def index2word(self): @index2word.setter def index2word(self, value): self.index_to_key = value -# -# @property -# def vocab(self): -# return self.map -# -# @vocab.setter -# def vocab(self, value): -# self.map = value @property - def novlookup(self): - """ pseudodict providing pseudovocab objects """ + def vocab(self): + raise NotImplementedError( + "The .vocab dict of 'Vocab' propery objects, one per key, has been removed.\n" + "See the KeyedVectors .key_to_index dict, .index_to_key list, and methods\n" + ".get_vecattr(key, attr)/.set_vecattr(key, attr, new_val) for replacement\n" + "functionality." + ) + + @vocab.setter + def vocab(self, value): + self.vocab() # trigger above NotImplementedError + + @property + def pseudovocab(self): + """ pseudodict providing pseudovocab objects + + not efficient, temp backcompat workaround 'just in case' a .vocab use can't adapt + """ class Vocaboid(object): def __init__(self, kv, index): self.kv = kv @@ -553,7 +561,7 @@ def __init__(self, kv): def __getitem__(self, key): return Vocaboid(self.data, self.data.get_index(key)) - def __contains(self, key): + def __contains__(self, key): return key in self.data return VocaboidDict(self) diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index 42b7a3d802..ab1fa008b3 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -56,7 +56,7 @@ from six.moves import zip, range from gensim import utils, matutils -from gensim.models.keyedvectors import Vocab, KeyedVectors +from gensim.models.keyedvectors import KeyedVectors try: from autograd import grad # Only required for optionally verifying gradients while training @@ -206,51 +206,49 @@ def build_vocab(self, relations, update=False): >>> model.train(epochs=50) """ - old_index2word_len = len(self.kv.index2word) + old_index_to_key_len = len(self.kv.index_to_key) logger.info("loading relations from train data..") for relation in relations: if len(relation) != 2: raise ValueError('Relation pair "%s" should have exactly two items' % repr(relation)) for item in relation: - if item in self.kv.vocab: - self.kv.vocab[item].count += 1 + if item in self.kv.key_to_index: + self.kv.set_vecattr(item, 'count', self.kv.get_vecattr(item, 'count') + 1) else: - self.kv.vocab[item] = Vocab(count=1, index=len(self.kv.index2word)) - self.kv.index2word.append(item) + self.kv.key_to_index[item] = len(self.kv.index_to_key) + self.kv.index_to_key.append(item) + self.kv.set_vecattr(item, 'count', 1) + node_1, node_2 = relation - node_1_index, node_2_index = self.kv.vocab[node_1].index, self.kv.vocab[node_2].index + node_1_index, node_2_index = self.kv.key_to_index[node_1], self.kv.key_to_index[node_2] self.node_relations[node_1_index].add(node_2_index) relation = (node_1_index, node_2_index) self.all_relations.append(relation) - logger.info("loaded %d relations from train data, %d nodes", len(self.all_relations), len(self.kv.vocab)) - self.indices_set = set(range(len(self.kv.index2word))) # Set of all node indices - self.indices_array = np.fromiter(range(len(self.kv.index2word)), dtype=int) # Numpy array of all node indices + logger.info("loaded %d relations from train data, %d nodes", len(self.all_relations), len(self.kv)) + self.indices_set = set(range(len(self.kv.index_to_key))) # Set of all node indices + self.indices_array = np.fromiter(range(len(self.kv.index_to_key)), dtype=int) # Numpy array of all node indices self._init_node_probabilities() if not update: self._init_embeddings() else: - self._update_embeddings(old_index2word_len) + self._update_embeddings(old_index_to_key_len) def _init_embeddings(self): """Randomly initialize vectors for the items in the vocab.""" - shape = (len(self.kv.index2word), self.size) + shape = (len(self.kv.index_to_key), self.size) self.kv.vectors = self._np_random.uniform(self.init_range[0], self.init_range[1], shape).astype(self.dtype) - def _update_embeddings(self, old_index2word_len): + def _update_embeddings(self, old_index_to_key_len): """Randomly initialize vectors for the items in the additional vocab.""" - shape = (len(self.kv.index2word) - old_index2word_len, self.size) + shape = (len(self.kv.index_to_key) - old_index_to_key_len, self.size) v = self._np_random.uniform(self.init_range[0], self.init_range[1], shape).astype(self.dtype) self.kv.vectors = np.concatenate([self.kv.vectors, v]) def _init_node_probabilities(self): """Initialize a-priori probabilities.""" - counts = np.fromiter(( - self.kv.vocab[self.kv.index2word[i]].count - for i in range(len(self.kv.index2word)) - ), - dtype=np.float64, count=len(self.kv.index2word)) + counts = self.kv.expandos['count'].astype(np.float64) self._node_counts_cumsum = np.cumsum(counts) self._node_probabilities = counts / counts.sum() @@ -288,14 +286,14 @@ def _sample_negatives(self, node_index): """ node_relations = self.node_relations[node_index] - num_remaining_nodes = len(self.kv.vocab) - len(node_relations) + num_remaining_nodes = len(self.kv) - len(node_relations) if num_remaining_nodes < self.negative: raise ValueError( 'Cannot sample %d negative nodes from a set of %d negative nodes for %s' % - (self.negative, num_remaining_nodes, self.kv.index2word[node_index]) + (self.negative, num_remaining_nodes, self.kv.index_to_key[node_index]) ) - positive_fraction = float(len(node_relations)) / len(self.kv.vocab) + positive_fraction = float(len(node_relations)) / len(self.kv) if positive_fraction < 0.01: # If number of positive relations is a small fraction of total nodes # re-sample till no positively connected nodes are chosen @@ -953,13 +951,13 @@ def closest_child(self, node): """ all_distances = self.distances(node) all_norms = np.linalg.norm(self.vectors, axis=1) - node_norm = all_norms[self.vocab[node].index] + node_norm = all_norms[self.get_index(node)] mask = node_norm >= all_norms if mask.all(): # No nodes lower in the hierarchy return None all_distances = np.ma.array(all_distances, mask=mask) closest_child_index = np.ma.argmin(all_distances) - return self.index2word[closest_child_index] + return self.index_to_key[closest_child_index] def closest_parent(self, node): """Get the node closest to `node` that is higher in the hierarchy than `node`. @@ -978,13 +976,13 @@ def closest_parent(self, node): """ all_distances = self.distances(node) all_norms = np.linalg.norm(self.vectors, axis=1) - node_norm = all_norms[self.vocab[node].index] + node_norm = all_norms[self.get_index(node)] mask = node_norm <= all_norms if mask.all(): # No nodes higher in the hierarchy return None all_distances = np.ma.array(all_distances, mask=mask) closest_child_index = np.ma.argmin(all_distances) - return self.index2word[closest_child_index] + return self.index_to_key[closest_child_index] def descendants(self, node, max_depth=5): """Get the list of recursively closest children from the given node, up to a max depth of `max_depth`. @@ -1155,11 +1153,11 @@ def most_similar(self, node_or_vector, topn=10, restrict_vocab=None): if not restrict_vocab: all_distances = self.distances(node_or_vector) else: - nodes_to_use = self.index2word[:restrict_vocab] + nodes_to_use = self.index_to_key[:restrict_vocab] all_distances = self.distances(node_or_vector, nodes_to_use) if isinstance(node_or_vector, string_types + (int,)): - node_index = self.vocab[node_or_vector].index + node_index = self.get_index(node_or_vector) else: node_index = None if not topn: @@ -1167,7 +1165,7 @@ def most_similar(self, node_or_vector, topn=10, restrict_vocab=None): else: closest_indices = matutils.argsort(all_distances, topn=1 + topn) result = [ - (self.index2word[index], float(all_distances[index])) + (self.index_to_key[index], float(all_distances[index])) for index in closest_indices if (not node_index or index != node_index) # ignore the input node ] if topn: @@ -1223,7 +1221,7 @@ def distances(self, node_or_vector, other_nodes=()): if not other_nodes: other_vectors = self.vectors else: - other_indices = [self.vocab[node].index for node in other_nodes] + other_indices = [self.get_index(node) for node in other_nodes] other_vectors = self.vectors[other_indices] return self.vector_distance_batch(input_vector, other_vectors) @@ -1424,14 +1422,13 @@ def __init__(self, file_path, embedding): """ items = set() - embedding_vocab = embedding.vocab relations = defaultdict(set) with utils.open(file_path, 'r') as f: reader = csv.reader(f, delimiter='\t') for row in reader: assert len(row) == 2, 'Hypernym pair has more than two items' - item_1_index = embedding_vocab[row[0]].index - item_2_index = embedding_vocab[row[1]].index + item_1_index = embedding.get_index(row[0]) + item_2_index = embedding.get_index(row[1]) relations[item_1_index].add(item_2_index) items.update([item_1_index, item_2_index]) self.items = items @@ -1502,7 +1499,7 @@ def evaluate_mean_rank_and_map(self, max_n=None): if item not in self.relations: continue item_relations = list(self.relations[item]) - item_term = self.embedding.index2word[item] + item_term = self.embedding.index_to_key[item] item_distances = self.embedding.distances(item_term) positive_relation_ranks, avg_precision = \ self.get_positive_relation_ranks_and_avg_prec(item_distances, item_relations) @@ -1530,7 +1527,6 @@ def __init__(self, train_path, test_path, embedding): """ items = set() - embedding_vocab = embedding.vocab relations = {'known': defaultdict(set), 'unknown': defaultdict(set)} data_files = {'known': train_path, 'unknown': test_path} for relation_type, data_file in data_files.items(): @@ -1538,8 +1534,8 @@ def __init__(self, train_path, test_path, embedding): reader = csv.reader(f, delimiter='\t') for row in reader: assert len(row) == 2, 'Hypernym pair has more than two items' - item_1_index = embedding_vocab[row[0]].index - item_2_index = embedding_vocab[row[1]].index + item_1_index = embedding.get_index(row[0]) + item_2_index = embedding.get_index(row[1]) relations[relation_type][item_1_index].add(item_2_index) items.update([item_1_index, item_2_index]) self.items = items @@ -1614,7 +1610,7 @@ def evaluate_mean_rank_and_map(self, max_n=None): continue unknown_relations = list(self.relations['unknown'][item]) known_relations = list(self.relations['known'][item]) - item_term = self.embedding.index2word[item] + item_term = self.embedding.index_to_key[item] item_distances = self.embedding.distances(item_term) unknown_relation_ranks, avg_precision = \ self.get_unknown_relation_ranks_and_avg_prec(item_distances, unknown_relations, known_relations) @@ -1727,7 +1723,7 @@ def create_vocab_trie(embedding): 'pygtrie could not be imported, please install pygtrie in order to use LexicalEntailmentEvaluation') vocab_trie = Trie() - for key in embedding.vocab: + for key in embedding.key_to_index: vocab_trie[key] = True return vocab_trie diff --git a/gensim/models/translation_matrix.py b/gensim/models/translation_matrix.py index 5801072bff..54b21416e3 100644 --- a/gensim/models/translation_matrix.py +++ b/gensim/models/translation_matrix.py @@ -151,12 +151,12 @@ def build(cls, lang_vec, lexicon=None): # if the lexicon is not provided, using the all the Keyedvectors's words as default for item in lexicon: words.append(item) - mat.append(lang_vec.vectors[lang_vec.vocab[item].index]) + mat.append(lang_vec.vectors[lang_vec.get_index(item)]) else: - for item in lang_vec.vocab.keys(): + for item in lang_vec.index_to_key: words.append(item) - mat.append(lang_vec.vectors[lang_vec.vocab[item].index]) + mat.append(lang_vec.vectors[lang_vec.get_index(item)]) return Space(mat, words) diff --git a/gensim/models/wrappers/varembed.py b/gensim/models/wrappers/varembed.py index 6f2920c175..cf76dbe13e 100644 --- a/gensim/models/wrappers/varembed.py +++ b/gensim/models/wrappers/varembed.py @@ -33,7 +33,7 @@ class VarEmbed(KeyedVectors): """ def __init__(self): - self.vector_size = 0 + super(VarEmbed, self).__init__(vector_size=0) self.vocab_size = 0 @classmethod @@ -95,13 +95,13 @@ def load_word_embeddings(self, word_embeddings, word_to_ix): self.vocab_size = len(counts) self.vector_size = word_embeddings.shape[1] self.vectors = np.zeros((self.vocab_size, self.vector_size)) - self.index_to_word = [None] * self.vocab_size - logger.info("Corpus has %i words", len(self.vocab)) + self.index_to_key = [None] * self.vocab_size + logger.info("Corpus has %i words", len(self)) for word_id, word in enumerate(counts): + self.index_to_key[word_id] = word self.key_to_index[word] = word_id - self.set_extra(word, 'count', counts[word]) + self.set_vecattr(word, 'count', counts[word]) self.vectors[word_id] = word_embeddings[word_to_ix[word]] - self.index2word[word_id] = word assert((len(self.key_to_index), self.vector_size) == self.vectors.shape) logger.info("Loaded matrix of %d size and %d dimensions", self.vocab_size, self.vector_size) diff --git a/gensim/similarities/nmslib.py b/gensim/similarities/nmslib.py index be0aedba28..4fad9761a5 100644 --- a/gensim/similarities/nmslib.py +++ b/gensim/similarities/nmslib.py @@ -187,7 +187,7 @@ def _build_from_doc2vec(self): """Build an NMSLIB index using document vectors from a Doc2Vec model.""" docvecs = self.model.dv - labels = [docvecs.index2key[i] for i in range(0, len(docvecs))] + labels = docvecs.index_to_key self._build_from_model(docvecs.vectors_norm, labels) def _build_from_keyedvectors(self): diff --git a/gensim/similarities/termsim.py b/gensim/similarities/termsim.py index 51dcb6971f..545858c8b1 100644 --- a/gensim/similarities/termsim.py +++ b/gensim/similarities/termsim.py @@ -145,7 +145,7 @@ def __init__(self, keyedvectors, threshold=0.0, exponent=2.0, kwargs=None): super(WordEmbeddingSimilarityIndex, self).__init__() def most_similar(self, t1, topn=10): - if t1 not in self.keyedvectors.vocab: + if t1 not in self.keyedvectors: logger.debug('an out-of-dictionary term "%s"', t1) else: most_similar = self.keyedvectors.most_similar(positive=[t1], topn=topn, **self.kwargs) diff --git a/gensim/test/test_keras_integration.py b/gensim/test/test_keras_integration.py index 3eb2841f58..913dde81e1 100644 --- a/gensim/test/test_keras_integration.py +++ b/gensim/test/test_keras_integration.py @@ -33,13 +33,13 @@ def testWord2VecTraining(self): Test word2vec training. """ model = self.model_cos_sim - self.assertTrue(model.wv.vectors.shape == (len(model.wv.vocab), 100)) - self.assertTrue(model.syn1.shape == (len(model.wv.vocab), 100)) + self.assertTrue(model.wv.vectors.shape == (len(model.wv), 100)) + self.assertTrue(model.syn1.shape == (len(model.wv), 100)) sims = model.wv.most_similar('graph', topn=10) # self.assertTrue(sims[0][0] == 'trees', sims) # most similar # test querying for "most similar" by vector - graph_vector = model.wv.vectors_norm[model.wv.vocab['graph'].index] + graph_vector = model.wv.vectors_norm[model.wv.get_index('graph')] sims2 = model.wv.most_similar(positive=[graph_vector], topn=11) sims2 = [(w, sim) for w, sim in sims2 if w != 'graph'] # ignore 'graph' itself self.assertEqual(sims, sims2) @@ -65,8 +65,8 @@ def testEmbeddingLayerCosineSim(self): word_a = 'graph' word_b = 'trees' output = model.predict([ - np.asarray([keras_w2v_model.wv.vocab[word_a].index]), - np.asarray([keras_w2v_model.wv.vocab[word_b].index]) + np.asarray([keras_w2v_model.wv.get_index(word_a)]), + np.asarray([keras_w2v_model.wv.get_index(word_b)]) ]) # output is the cosine distance between the two words (as a similarity measure) diff --git a/gensim/test/test_poincare.py b/gensim/test/test_poincare.py index f0520d0a7f..67b2668e02 100644 --- a/gensim/test/test_poincare.py +++ b/gensim/test/test_poincare.py @@ -57,24 +57,24 @@ def setUp(self): self.data_large = PoincareRelations(datapath('poincare_hypernyms_large.tsv')) def models_equal(self, model_1, model_2): - self.assertEqual(len(model_1.kv.vocab), len(model_2.kv.vocab)) - self.assertEqual(set(model_1.kv.vocab.keys()), set(model_2.kv.vocab.keys())) + self.assertEqual(len(model_1.kv), len(model_2.kv)) + self.assertEqual(set(model_1.kv.index_to_key), set(model_2.kv.index_to_key)) self.assertTrue(np.allclose(model_1.kv.vectors, model_2.kv.vectors)) def test_data_counts(self): """Tests whether data has been loaded correctly and completely.""" model = PoincareModel(self.data) self.assertEqual(len(model.all_relations), 5) - self.assertEqual(len(model.node_relations[model.kv.vocab['kangaroo.n.01'].index]), 3) - self.assertEqual(len(model.kv.vocab), 7) + self.assertEqual(len(model.node_relations[model.kv.get_index('kangaroo.n.01')]), 3) + self.assertEqual(len(model.kv), 7) self.assertTrue('mammal.n.01' not in model.node_relations) def test_data_counts_with_bytes(self): """Tests whether input bytes data is loaded correctly and completely.""" model = PoincareModel([(b'\x80\x01c', b'\x50\x71a'), (b'node.1', b'node.2')]) self.assertEqual(len(model.all_relations), 2) - self.assertEqual(len(model.node_relations[model.kv.vocab[b'\x80\x01c'].index]), 1) - self.assertEqual(len(model.kv.vocab), 4) + self.assertEqual(len(model.node_relations[model.kv.get_index(b'\x80\x01c')]), 1) + self.assertEqual(len(model.kv), 4) self.assertTrue(b'\x50\x71a' not in model.node_relations) def test_persistence(self): @@ -96,12 +96,12 @@ def test_persistence_separate_file(self): def test_online_learning(self): """Tests whether additional input data is loaded correctly and completely.""" model = PoincareModel(self.data, burn_in=0, negative=3) - self.assertEqual(len(model.kv.vocab), 7) - self.assertEqual(model.kv.vocab['kangaroo.n.01'].count, 3) - self.assertEqual(model.kv.vocab['cat.n.01'].count, 1) + self.assertEqual(len(model.kv), 7) + self.assertEqual(model.kv.get_vecattr('kangaroo.n.01', 'count'), 3) + self.assertEqual(model.kv.get_vecattr('cat.n.01', 'count'), 1) model.build_vocab([('kangaroo.n.01', 'cat.n.01')], update=True) # update vocab - self.assertEqual(model.kv.vocab['kangaroo.n.01'].count, 4) - self.assertEqual(model.kv.vocab['cat.n.01'].count, 2) + self.assertEqual(model.kv.get_vecattr('kangaroo.n.01', 'count'), 4) + self.assertEqual(model.kv.get_vecattr('cat.n.01', 'count'), 2) def test_train_after_load(self): """Tests whether the model can be trained correctly after loading from disk.""" @@ -117,7 +117,7 @@ def test_persistence_old_model(self): """Tests whether model from older gensim version is loaded correctly.""" loaded = PoincareModel.load(datapath('poincare_test_3.4.0')) self.assertEqual(loaded.kv.vectors.shape, (239, 2)) - self.assertEqual(len(loaded.kv.vocab), 239) + self.assertEqual(len(loaded.kv), 239) self.assertEqual(loaded.size, 2) self.assertEqual(len(loaded.all_relations), 200) @@ -268,7 +268,7 @@ def test_most_similar_topn(self): self.assertEqual(len(self.vectors.most_similar('dog.n.01', topn=10)), 10) predicted = self.vectors.most_similar('dog.n.01', topn=None) - self.assertEqual(len(predicted), len(self.vectors.vocab) - 1) + self.assertEqual(len(predicted), len(self.vectors) - 1) self.assertEqual(predicted[-1][0], 'gallant_fox.n.01') def test_most_similar_raises_keyerror(self): @@ -311,7 +311,7 @@ def test_distances(self): self.assertTrue(np.allclose(distances, [4.5278745, 0])) distances = self.vectors.distances('dog.n.01') - self.assertEqual(len(distances), len(self.vectors.vocab)) + self.assertEqual(len(distances), len(self.vectors)) self.assertTrue(np.allclose(distances[-1], 10.04756)) def test_distances_with_vector_input(self): @@ -321,7 +321,7 @@ def test_distances_with_vector_input(self): self.assertTrue(np.allclose(distances, [4.5278745, 0])) distances = self.vectors.distances(input_vector) - self.assertEqual(len(distances), len(self.vectors.vocab)) + self.assertEqual(len(distances), len(self.vectors)) self.assertTrue(np.allclose(distances[-1], 10.04756)) def test_poincare_distances_batch(self): diff --git a/gensim/test/test_similarities.py b/gensim/test/test_similarities.py index e5ab6a10ac..4657191d31 100644 --- a/gensim/test/test_similarities.py +++ b/gensim/test/test_similarities.py @@ -302,7 +302,7 @@ def setUp(self): class TestWmdSimilarity(unittest.TestCase, _TestSimilarityABC): def setUp(self): self.cls = similarities.WmdSimilarity - self.w2v_model = Word2Vec(texts, min_count=1) + self.w2v_model = Word2Vec(texts, min_count=1).wv def factoryMethod(self): # Override factoryMethod. @@ -621,7 +621,7 @@ def assertAllSimilaritiesDisableIndexer(self, model, wv, index): exact_similarities = model.most_similar(positive=[vector], topn=None) self.assertEqual(approx_similarities, exact_similarities) - self.assertEqual(len(approx_similarities), len(wv.vectors.vocab)) + self.assertEqual(len(approx_similarities), len(wv.vectors)) def assertIndexSaved(self, index): fname = get_tmpfile('gensim_similarities.tst.pkl') @@ -1262,7 +1262,7 @@ def test_most_similar(self): # check that the term itself is not returned index = WordEmbeddingSimilarityIndex(self.vectors) - terms = [term for term, similarity in index.most_similar(u"holiday", topn=len(self.vectors.vocab))] + terms = [term for term, similarity in index.most_similar(u"holiday", topn=len(self.vectors))] self.assertFalse(u"holiday" in terms) # check that the threshold works as expected diff --git a/gensim/test/test_varembed_wrapper.py b/gensim/test/test_varembed_wrapper.py index 54401a15e6..9d0a16d6e3 100644 --- a/gensim/test/test_varembed_wrapper.py +++ b/gensim/test/test_varembed_wrapper.py @@ -48,7 +48,7 @@ def testSimilarity(self): def model_sanity(self, model): """Check vocabulary and vector size""" self.assertEqual(model.vectors.shape, (model.vocab_size, model.vector_size)) - self.assertTrue(model.vectors.shape[0] == len(model.vocab)) + self.assertTrue(model.vectors.shape[0] == len(model)) @unittest.skipIf(sys.version_info < (2, 7), 'Supported only on Python 2.7 and above') def testAddMorphemesToEmbeddings(self): diff --git a/gensim/test/test_wordrank_wrapper.py b/gensim/test/test_wordrank_wrapper.py index dc565001fa..b5b4a2b489 100644 --- a/gensim/test/test_wordrank_wrapper.py +++ b/gensim/test/test_wordrank_wrapper.py @@ -38,7 +38,7 @@ def testLoadWordrankFormat(self): model = wordrank.Wordrank.load_wordrank_model(self.wr_file) vocab_size, dim = 76, 50 self.assertEqual(model.vectors.shape, (vocab_size, dim)) - self.assertEqual(len(model.vocab), vocab_size) + self.assertEqual(len(model), vocab_size) os.remove(self.wr_file + '.w2vformat') def testEnsemble(self): @@ -71,8 +71,8 @@ def testLookup(self): self.assertTrue(numpy.allclose(self.test_model['night'], self.test_model[['night']])) def models_equal(self, model, model2): - self.assertEqual(len(model.vocab), len(model2.vocab)) - self.assertEqual(set(model.vocab.keys()), set(model2.vocab.keys())) + self.assertEqual(len(model), len(model2)) + self.assertEqual(set(model.index_to_key), set(model2.index_to_key)) self.assertTrue(numpy.allclose(model.syn0, model2.syn0)) diff --git a/gensim/topic_coherence/text_analysis.py b/gensim/topic_coherence/text_analysis.py index 79ffc132fd..477a9f2bc3 100644 --- a/gensim/topic_coherence/text_analysis.py +++ b/gensim/topic_coherence/text_analysis.py @@ -621,7 +621,7 @@ def __init__(self, relevant_ids, dictionary, model=None, **model_kwargs): def not_in_vocab(self, words): uniq_words = set(utils.flatten(words)) - return set(word for word in uniq_words if word not in self.model.vocab) + return set(word for word in uniq_words if word not in self.model) def get_occurrences(self, word): """Return number of docs the word occurs in, once `accumulate` has been called.""" @@ -629,7 +629,7 @@ def get_occurrences(self, word): self.token2id[word] # is this a token or an id? except KeyError: word = self.dictionary.id2token[word] - return self.model.vocab[word].count + return self.model.get_vecattr(word, 'count') def get_co_occurrences(self, word1, word2): """Return number of docs the words co-occur in, once `accumulate` has been called.""" @@ -663,4 +663,4 @@ def _words_with_embeddings(self, ids): ids = [ids] words = [self.dictionary.id2token[word_id] for word_id in ids] - return [word for word in words if word in self.model.vocab] + return [word for word in words if word in self.model] From 4657b148d2dcd3de6f89adcb6dc636106bac39d4 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Fri, 15 May 2020 13:04:16 -0700 Subject: [PATCH 30/60] enable running inside '-m mtprof' (or cProfile) via explicit unittest.main(module=..) --- gensim/test/test_doc2vec.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/test/test_doc2vec.py b/gensim/test/test_doc2vec.py index ccafa8004d..aa958b744d 100644 --- a/gensim/test/test_doc2vec.py +++ b/gensim/test/test_doc2vec.py @@ -838,4 +838,4 @@ def read_su_sentiment_rotten_tomatoes(dirname, lowercase=True): if __name__ == '__main__': logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) - unittest.main() + unittest.main(module='gensim.test.test_doc2vec') From b5ff29bf4bb8dd26bb250e9185f8aceb782d29cc Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Fri, 15 May 2020 13:02:12 -0700 Subject: [PATCH 31/60] faster sample_int reads --- gensim/models/doc2vec_inner.pyx | 13 ++++++++++--- gensim/models/fasttext_inner.pyx | 4 +++- gensim/models/word2vec_inner.pyx | 10 +++++++--- 3 files changed, 20 insertions(+), 7 deletions(-) diff --git a/gensim/models/doc2vec_inner.pyx b/gensim/models/doc2vec_inner.pyx index 4f58c69d90..e462110960 100644 --- a/gensim/models/doc2vec_inner.pyx +++ b/gensim/models/doc2vec_inner.pyx @@ -328,12 +328,14 @@ def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, cdef int i, j cdef long result = 0 + cdef np.uint32_t *vocab_sample_ints init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=train_words, work=work, neu1=None, word_vectors=word_vectors, word_locks=word_locks, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) - vocab_sample_ints = model.wv.expandos['sample_int'] + if c.sample: + vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) if c.hs: vocab_codes = model.wv.expandos['code'] vocab_points = model.wv.expandos['point'] @@ -456,12 +458,15 @@ def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=N cdef REAL_t count, inv_count = 1.0 cdef int i, j, k, m cdef long result = 0 + cdef np.uint32_t *vocab_sample_ints init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=False, work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) - vocab_sample_ints = model.wv.expandos['sample_int'] + if c.sample: + vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) +# vocab_sample_ints = model.wv.expandos['sample_int'] # this variant noticeably slower if c.hs: vocab_codes = model.wv.expandos['code'] vocab_points = model.wv.expandos['point'] @@ -597,11 +602,13 @@ def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, cdef int i, j, k, m, n cdef long result = 0 + cdef np.uint32_t *vocab_sample_ints init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=False, work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) - vocab_sample_ints = model.wv.expandos['sample_int'] + if c.sample: + vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) if c.hs: vocab_codes = model.wv.expandos['code'] vocab_points = model.wv.expandos['point'] diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index cd19a00428..e00a2128f4 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -513,9 +513,11 @@ cdef object populate_ft_config(FastTextConfig *c, wv, buckets_word, sentences): """ cdef int effective_words = 0 cdef int effective_sentences = 0 + cdef np.uint32_t *vocab_sample_ints c.sentence_idx[0] = 0 # indices of the first sentence always start at 0 - vocab_sample_ints = wv.expandos['sample_int'] + if c.sample: + vocab_sample_ints = np.PyArray_DATA(wv.expandos['sample_int']) if c.hs: vocab_codes = wv.expandos['code'] vocab_points = wv.expandos['point'] diff --git a/gensim/models/word2vec_inner.pyx b/gensim/models/word2vec_inner.pyx index 222ca9a254..63095470be 100755 --- a/gensim/models/word2vec_inner.pyx +++ b/gensim/models/word2vec_inner.pyx @@ -482,7 +482,7 @@ cdef init_w2v_config(Word2VecConfig *c, model, alpha, compute_loss, _work, _neu1 if c[0].hs: c[0].syn1 = (np.PyArray_DATA(model.syn1)) - + if c[0].negative: c[0].syn1neg = (np.PyArray_DATA(model.syn1neg)) c[0].cum_table = (np.PyArray_DATA(model.cum_table)) @@ -526,9 +526,11 @@ def train_batch_sg(model, sentences, alpha, _work, compute_loss): cdef int i, j, k cdef int effective_words = 0, effective_sentences = 0 cdef int sent_idx, idx_start, idx_end + cdef np.uint32_t *vocab_sample_ints init_w2v_config(&c, model, alpha, compute_loss, _work) - vocab_sample_ints = model.wv.expandos['sample_int'] + if c.sample: + vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) if c.hs: vocab_codes = model.wv.expandos['code'] vocab_points = model.wv.expandos['point'] @@ -620,9 +622,11 @@ def train_batch_cbow(model, sentences, alpha, _work, _neu1, compute_loss): cdef int i, j, k cdef int effective_words = 0, effective_sentences = 0 cdef int sent_idx, idx_start, idx_end + cdef np.uint32_t *vocab_sample_ints init_w2v_config(&c, model, alpha, compute_loss, _work, _neu1) - vocab_sample_ints = model.wv.expandos['sample_int'] + if c.sample: + vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) if c.hs: vocab_codes = model.wv.expandos['code'] vocab_points = model.wv.expandos['point'] From 098119b00dac2ce38ff851016b8b1a39525c12b7 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 18 May 2020 22:22:14 -0700 Subject: [PATCH 32/60] load_word2vec_format(.., no_header=True) to support GLoVe text vectors --- gensim/models/doc2vec.py | 6 +-- gensim/models/keyedvectors.py | 71 ++++++++++++++++++++++++-------- gensim/scripts/glove2word2vec.py | 13 +++--- gensim/test/test_keyedvectors.py | 33 ++++++++++++++- 4 files changed, 95 insertions(+), 28 deletions(-) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 7ea7b17927..4774503b82 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -745,15 +745,15 @@ def save_word2vec_format(self, fname, doctag_vec=False, word_vec=True, prefix='* self.wv.save_word2vec_format(fname, fvocab, binary, total_vec) # save document vectors if doctag_vec: - write_first_line = True + write_header = True append = False if word_vec: # simply appending to existing file - write_first_line = False + write_header = False append = True self.dv.save_word2vec_format( fname, prefix=prefix, fvocab=fvocab, binary=binary, - write_first_line=write_first_line, append=append, + write_header=write_header, append=append, sort_attr='doc_count') def init_sims(self, replace=False): diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 611accdaa8..e9a55dac31 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -163,8 +163,10 @@ """ -from itertools import chain import logging +import sys +import itertools +from itertools import chain from collections import UserList, UserDict from numbers import Integral @@ -1385,7 +1387,7 @@ def relative_cosine_similarity(self, wa, wb, topn=10): return rcs - def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, write_first_line=True, + def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, write_header=True, prefix='', append=False, sort_attr='count'): """Store the input-hidden weight matrix in the same format used by the original C word2vec-tool, for compatibility. @@ -1401,17 +1403,23 @@ def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, total_vec : int, optional Explicitly specify total number of vectors (in case word vectors are appended with document vectors afterwards). - TODO: doc other params + write_header : bool, optional + If False, don't write the 1st line declaring the count of vectors and dimensions. + TODO: doc prefix, append, sort_attr """ if total_vec is None: total_vec = len(self.index_to_key) mode = 'wb' if not append else 'ab' - sorted_vocab_keys = sorted(self.key_to_index.keys(), key=lambda k: -self.get_vecattr(k, sort_attr)) + if 'count' in self.expandos: + # if frequency-info available, store in most-to-least-frequent order + store_order_vocab_keys = sorted(self.key_to_index.keys(), key=lambda k: -self.get_vecattr(k, sort_attr)) + else: + store_order_vocab_keys = self.index_to_key if fvocab is not None: logger.info("storing vocabulary in %s", fvocab) with utils.open(fvocab, mode) as vout: - for word in sorted_vocab_keys: + for word in store_order_vocab_keys: vout.write(utils.to_utf8("%s%s %s\n" % (prefix, word, self.get_vecattr(word, sort_attr)))) logger.info("storing %sx%s projection weights into %s", total_vec, self.vector_size, fname) @@ -1424,10 +1432,10 @@ def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, if not (i == val): break index_id_count += 1 - keys_to_write = chain(range(0, index_id_count), sorted_vocab_keys) + keys_to_write = chain(range(0, index_id_count), store_order_vocab_keys) with utils.open(fname, mode) as fout: - if write_first_line: + if write_header: fout.write(utils.to_utf8("%s %s\n" % (total_vec, self.vector_size))) for key in keys_to_write: row = self[key] @@ -1439,7 +1447,7 @@ def save_word2vec_format(self, fname, fvocab=None, binary=False, total_vec=None, @classmethod def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', - limit=None, datatype=REAL): + limit=None, datatype=REAL, no_header=False): """Load the input-hidden weight matrix from the original C word2vec-tool format. Warnings @@ -1470,7 +1478,11 @@ def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', 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.) - + no_header : bool, optional + Default False means a usual word2ve-format file, with a 1st line declaring the count of + following vectors & number of dimensions. If True, the file is assumed lack a declaratory + (vocab_size, vector_size) header and instead start with the 1st vector, and an extra + reading-pass will be used to discover the number of vectors. Works only with `binary=False`. Returns ------- :class:`~gensim.models.keyedvectors.KeyedVectors` @@ -1479,7 +1491,7 @@ def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', """ return _load_word2vec_format( cls, fname, fvocab=fvocab, binary=binary, encoding=encoding, unicode_errors=unicode_errors, - limit=limit, datatype=datatype) + limit=limit, datatype=datatype, no_header=no_header) def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='utf8', unicode_errors='strict'): """Merge in an input-hidden weight matrix loaded from the original C word2vec-tool format, @@ -1698,15 +1710,31 @@ def _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, line = fin.readline() if line == b'': raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") - parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") - if len(parts) != vector_size + 1: - raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no) - word, weights = parts[0], [datatype(x) for x in parts[1:]] + word, weights = _word2vec_line_to_vector(line, datatype, unicode_errors, encoding) _add_word_to_result(result, counts, word, weights, vocab_size) +def _word2vec_line_to_vector(line, datatype, unicode_errors, encoding): + parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") + word, weights = parts[0], [datatype(x) for x in parts[1:]] + return word, weights + + +def _word2vec_detect_sizes_text(fin, limit, datatype, unicode_errors, encoding): + vector_size = None + for vocab_size in itertools.count(): + line = fin.readline() + if line == b'' or vocab_size == limit: # EOF/max: return what we've got + break + if vector_size: + continue # don't bother parsing lines past the 1st + word, weights = _word2vec_line_to_vector(line, datatype, unicode_errors, encoding) + vector_size = len(weights) + return vocab_size, vector_size + + def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict', - limit=None, datatype=REAL, binary_chunk_size=100 * 1024): + limit=sys.maxsize, datatype=REAL, no_header=False, binary_chunk_size=100 * 1024): """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), @@ -1756,8 +1784,17 @@ def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8' logger.info("loading projection weights from %s", fname) with utils.open(fname, 'rb') as fin: - header = utils.to_unicode(fin.readline(), encoding=encoding) - vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format + if no_header: + # deduce both vocab_size & vector_size from 1st pass over file + if binary: + raise NotImplementedError("no_header only available for text-format files") + else: # text + vocab_size, vector_size = _word2vec_detect_sizes_text(fin, limit, datatype, unicode_errors, encoding) + fin.close() + fin = utils.open(fname, 'rb') + else: + header = utils.to_unicode(fin.readline(), encoding=encoding) + vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format if limit: vocab_size = min(vocab_size, limit) result = cls(vector_size) diff --git a/gensim/scripts/glove2word2vec.py b/gensim/scripts/glove2word2vec.py index 836b0e6b8f..7640b60ec9 100644 --- a/gensim/scripts/glove2word2vec.py +++ b/gensim/scripts/glove2word2vec.py @@ -60,6 +60,8 @@ import argparse from gensim import utils +from gensim.utils import deprecated +from gensim.models.keyedvectors import KeyedVectors logger = logging.getLogger(__name__) @@ -85,6 +87,7 @@ def get_glove_info(glove_file_name): return num_lines, num_dims +@deprecated("KeyedVectors.load_word2vec_format(.., binary=False, no_header=True) loads GLoVE text vectors.") def glove2word2vec(glove_input_file, word2vec_output_file): """Convert `glove_input_file` in GloVe format to word2vec format and write it to `word2vec_output_file`. @@ -101,13 +104,11 @@ def glove2word2vec(glove_input_file, word2vec_output_file): Number of vectors (lines) of input file and its dimension. """ - num_lines, num_dims = get_glove_info(glove_input_file) + glovekv = KeyedVectors.load_word2vec_format(glove_input_file, binary=False, no_header=True) + + num_lines, num_dims = len(glovekv), glovekv.vector_size logger.info("converting %i vectors from %s to %s", num_lines, glove_input_file, word2vec_output_file) - with utils.open(word2vec_output_file, 'wb') as fout: - fout.write("{0} {1}\n".format(num_lines, num_dims).encode('utf-8')) - with utils.open(glove_input_file, 'rb') as fin: - for line in fin: - fout.write(line) + glovekv.save_word2vec_format(word2vec_output_file, binary=False) return num_lines, num_dims diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py index 36f3ac48c5..c5054bd735 100644 --- a/gensim/test/test_keyedvectors.py +++ b/gensim/test/test_keyedvectors.py @@ -14,7 +14,7 @@ import numpy as np -from gensim.models.keyedvectors import KeyedVectors, REAL +from gensim.models.keyedvectors import KeyedVectors, REAL, pseudorandom_weak_vector from gensim.test.utils import datapath import gensim.models.keyedvectors @@ -24,7 +24,7 @@ class TestKeyedVectors(unittest.TestCase): def setUp(self): self.vectors = KeyedVectors.load_word2vec_format( - datapath('euclidean_vectors.bin'), binary=True, datatype=np.float64) + datapath('euclidean_vectors.bin'), binary=True) self.model_path = datapath("w2v_keyedvectors_load_test.modeldata") self.vocab_path = datapath("w2v_keyedvectors_load_test.vocab") @@ -265,6 +265,35 @@ def test_load_model_and_vocab_file_ignore(self): self.assertTrue(np.array_equal( model.get_vector(u'どういたしまして'), np.array([.1, .2, .3], dtype=np.float32))) + def test_save_reload(self): + randkv = KeyedVectors(vector_size=100) + count = 20 + keys = [str(i) for i in range(count)] + weights = [pseudorandom_weak_vector(randkv.vector_size) for _ in range(count)] + randkv.add(keys, weights) + tmpfiletxt = gensim.test.utils.get_tmpfile("tmp_kv.txt") + randkv.save_word2vec_format(tmpfiletxt, binary=False) + reloadtxtkv = KeyedVectors.load_word2vec_format(tmpfiletxt, binary=False) + self.assertEqual(randkv.index_to_key, reloadtxtkv.index_to_key) + self.assertTrue((randkv.vectors == reloadtxtkv.vectors).all()) + tmpfilebin = gensim.test.utils.get_tmpfile("tmp_kv.bin") + randkv.save_word2vec_format(tmpfilebin, binary=True) + reloadbinkv = KeyedVectors.load_word2vec_format(tmpfilebin, binary=True) + self.assertEqual(randkv.index_to_key, reloadbinkv.index_to_key) + self.assertTrue((randkv.vectors == reloadbinkv.vectors).all()) + + def test_no_header(self): + randkv = KeyedVectors(vector_size=100) + count = 20 + keys = [str(i) for i in range(count)] + weights = [pseudorandom_weak_vector(randkv.vector_size) for _ in range(count)] + randkv.add(keys, weights) + tmpfiletxt = gensim.test.utils.get_tmpfile("tmp_kv.txt") + randkv.save_word2vec_format(tmpfiletxt, binary=False, write_header=False) + reloadtxtkv = KeyedVectors.load_word2vec_format(tmpfiletxt, binary=False, no_header=True) + self.assertEqual(randkv.index_to_key, reloadtxtkv.index_to_key) + self.assertTrue((randkv.vectors == reloadtxtkv.vectors).all()) + class Gensim320Test(unittest.TestCase): def test(self): From 318a8587747708b2d0aa2291ba6339011330a159 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 26 May 2020 12:42:47 -0700 Subject: [PATCH 33/60] refactor & comment lockf feature; allow single-element lockf --- gensim/models/doc2vec.py | 35 ++++------ gensim/models/doc2vec_corpusfile.pyx | 72 +++++++++---------- gensim/models/doc2vec_inner.pxd | 13 ++-- gensim/models/doc2vec_inner.pyx | 99 ++++++++++++++------------- gensim/models/fasttext.py | 29 +++++--- gensim/models/fasttext_inner.pxd | 16 +++-- gensim/models/fasttext_inner.pyx | 49 ++++++++----- gensim/models/keyedvectors.py | 4 +- gensim/models/word2vec.py | 6 +- gensim/models/word2vec_corpusfile.pyx | 12 ++-- gensim/models/word2vec_inner.pxd | 19 ++--- gensim/models/word2vec_inner.pyx | 41 +++++------ gensim/test/test_fasttext.py | 4 +- gensim/test/test_word2vec.py | 6 +- 14 files changed, 221 insertions(+), 184 deletions(-) diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 4774503b82..620ee95a43 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -74,8 +74,7 @@ from timeit import default_timer from dataclasses import dataclass -from numpy import zeros, float32 as REAL, ones, \ - memmap as np_memmap, vstack, integer, dtype +from numpy import zeros, float32 as REAL, vstack, integer, dtype import numpy as np from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc @@ -338,13 +337,7 @@ def reset_weights(self): super(Doc2Vec, self).reset_weights() self.dv.resize_vectors() self.dv.randomly_initialize_vectors() - if self.dv.mapfile_path: - self.dv.vectors_lockf = np_memmap( - self.dv.mapfile_path + '.vectors_lockf', dtype=REAL, mode='w+', shape=(len(self.dv.vectors),) - ) - self.dv.vectors_lockf.fill(1.0) - else: - self.dv.vectors_lockf = ones((len(self.dv.vectors),), dtype=REAL) # zeros suppress learning + self.dv.vectors_lockf = np.ones(1, dtype=REAL) # 0.0 values suppress word-backprop-updates; 1.0 allows def reset_from(self, other_model): """Copy shareable data structures from another (possibly pre-trained) model. @@ -375,7 +368,7 @@ def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_p total_examples=None, total_words=None, offsets=None, start_doctags=None, **kwargs): work, neu1 = thread_private_mem doctag_vectors = self.dv.vectors - doctag_locks = self.dv.vectors_lockf + doctags_lockf = self.dv.vectors_lockf offset = offsets[thread_id] start_doctag = start_doctags[thread_id] @@ -384,17 +377,17 @@ def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_p examples, tally, raw_tally = d2v_train_epoch_dbow( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, total_examples, total_words, work, neu1, len(self.dv), - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks, train_words=self.dbow_words) + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf, train_words=self.dbow_words) elif self.dm_concat: examples, tally, raw_tally = d2v_train_epoch_dm_concat( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, total_examples, total_words, work, neu1, len(self.dv), - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf) else: examples, tally, raw_tally = d2v_train_epoch_dm( self, corpus_file, offset, start_doctag, cython_vocab, cur_epoch, total_examples, total_words, work, neu1, len(self.dv), - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf) return examples, tally, raw_tally @@ -421,21 +414,21 @@ def _do_train_job(self, job, alpha, inits): for doc in job: doctag_indexes = [self.dv.get_index(tag) for tag in doc.tags if tag in self.dv] doctag_vectors = self.dv.vectors - doctag_locks = self.dv.vectors_lockf + doctags_lockf = self.dv.vectors_lockf 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 + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf ) 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 + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf ) else: tally += train_document_dm( self, doc.words, doctag_indexes, alpha, work, neu1, - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf ) return tally, self._raw_word_count(job) @@ -628,7 +621,7 @@ def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, steps doctag_vectors = pseudorandom_weak_vector(self.dv.vector_size, seed_string=' '.join(doc_words)) doctag_vectors = doctag_vectors.reshape(1, self.dv.vector_size) - doctag_locks = np.ones(1, dtype=REAL) + doctags_lockf = np.ones(1, dtype=REAL) doctag_indexes = [0] work = zeros(self.layer1_size, dtype=REAL) if not self.sg: @@ -640,17 +633,17 @@ def infer_vector(self, doc_words, alpha=None, min_alpha=None, epochs=None, 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 + learn_words=False, learn_hidden=False, doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf ) 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 + learn_words=False, learn_hidden=False, doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf ) 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 + learn_words=False, learn_hidden=False, doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf ) alpha -= alpha_delta diff --git a/gensim/models/doc2vec_corpusfile.pyx b/gensim/models/doc2vec_corpusfile.pyx index 13ceb4aa4e..4b50dd0125 100644 --- a/gensim/models/doc2vec_corpusfile.pyx +++ b/gensim/models/doc2vec_corpusfile.pyx @@ -93,9 +93,9 @@ cdef void prepare_c_structures_for_batch(vector[string] &doc_words, int sample, def d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, - _expected_words, work, neu1, docvecs_count, word_vectors=None, word_locks=None, + _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, train_words=False, learn_doctags=True, learn_words=True, learn_hidden=True, - doctag_vectors=None, doctag_locks=None): + doctag_vectors=None, doctags_lockf=None): """Train distributed bag of words model ("PV-DBOW") by training on a corpus file. Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train`. @@ -124,13 +124,13 @@ def d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab Whether or not the weights of the hidden layer will be updated. word_vectors : numpy.ndarray, optional The vector representation for each word in the vocabulary. If None, these will be retrieved from the model. - word_locks : numpy.ndarray, optional - A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates, - a value of 1 allows to update word-vectors. + words_lockf : numpy.ndarray, optional + EXPERIMENTAL. A learning lock factor for each word-vector, value 0.0 completely blocks updates, a value + of 1.0 allows normal updates to word-vectors. doctag_vectors : numpy.ndarray, optional Vector representations of the tags. If None, these will be retrieved from the model. - doctag_locks : numpy.ndarray, optional - The lock factors for each tag, same as `word_locks`, but for document-vectors. + doctags_lockf : numpy.ndarray, optional + EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors. Returns ------- @@ -162,8 +162,8 @@ def d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab init_d2v_config( &c, model, _alpha, learn_doctags, learn_words, learn_hidden, train_words=train_words, - work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks, docvecs_count=docvecs_count) + work=work, neu1=neu1, word_vectors=word_vectors, words_lockf=words_lockf, + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf, docvecs_count=docvecs_count) # release GIL & train on the full corpus, document by document with nogil: @@ -196,27 +196,29 @@ def d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab # we reuse the DBOW function, as it is equivalent to skip-gram for this purpose fast_document_dbow_hs( c.points[i], c.codes[i], c.codelens[i], c.word_vectors, c.syn1, c.layer1_size, - c.indexes[j], c.alpha, c.work, c.learn_words, c.learn_hidden, c.word_locks) + c.indexes[j], c.alpha, c.work, c.learn_words, c.learn_hidden, c.words_lockf, + c.words_lockf_len) if c.negative: # we reuse the DBOW function, as it is equivalent to skip-gram for this purpose c.next_random = fast_document_dbow_neg( c.negative, c.cum_table, c.cum_table_len, c.word_vectors, c.syn1neg, c.layer1_size, c.indexes[i], c.indexes[j], c.alpha, c.work, - c.next_random, c.learn_words, c.learn_hidden, c.word_locks) + c.next_random, c.learn_words, c.learn_hidden, c.words_lockf, c.words_lockf_len) # docvec-training if _doc_tag < c.docvecs_count: if c.hs: fast_document_dbow_hs( c.points[i], c.codes[i], c.codelens[i], c.doctag_vectors, c.syn1, c.layer1_size, - _doc_tag, c.alpha, c.work, c.learn_doctags, c.learn_hidden, c.doctag_locks) + _doc_tag, c.alpha, c.work, c.learn_doctags, c.learn_hidden, c.doctags_lockf, + c.doctags_lockf_len) if c.negative: c.next_random = fast_document_dbow_neg( c.negative, c.cum_table, c.cum_table_len, c.doctag_vectors, c.syn1neg, c.layer1_size, c.indexes[i], _doc_tag, c.alpha, c.work, c.next_random, - c.learn_doctags, c.learn_hidden, c.doctag_locks) + c.learn_doctags, c.learn_hidden, c.doctags_lockf, c.doctags_lockf_len) total_documents += 1 total_effective_words += effective_words @@ -230,8 +232,8 @@ def d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab def d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, - _expected_words, work, neu1, docvecs_count, word_vectors=None, word_locks=None, - learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, doctag_locks=None): + _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, + learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, doctags_lockf=None): """Train distributed memory model ("PV-DM") by training on a corpus file. 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. @@ -259,13 +261,13 @@ def d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, Whether or not the weights of the hidden layer will be updated. word_vectors : numpy.ndarray, optional The vector representation for each word in the vocabulary. If None, these will be retrieved from the model. - word_locks : numpy.ndarray, optional - A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates, - a value of 1 allows to update word-vectors. + words_lockf : numpy.ndarray, optional + EXPERIMENTAL. A learning lock factor for each word-vector, value 0.0 completely blocks updates, a value + of 1.0 allows normal updates to word-vectors. doctag_vectors : numpy.ndarray, optional Vector representations of the tags. If None, these will be retrieved from the model. - doctag_locks : numpy.ndarray, optional - The lock factors for each tag, same as `word_locks`, but for document-vectors. + doctags_lockf : numpy.ndarray, optional + EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors. Returns ------- @@ -298,8 +300,8 @@ def d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, init_d2v_config( &c, model, _alpha, learn_doctags, learn_words, learn_hidden, train_words=False, - work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks, docvecs_count=docvecs_count) + work=work, neu1=neu1, word_vectors=word_vectors, words_lockf=words_lockf, + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf, docvecs_count=docvecs_count) # release GIL & train on the full corpus, document by document with nogil: @@ -357,14 +359,14 @@ def d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, sscal(&c.layer1_size, &inv_count, c.work, &ONE) # (does this need BLAS-variants like saxpy?) # apply accumulated error in work if c.learn_doctags and _doc_tag < c.docvecs_count: - our_saxpy(&c.layer1_size, &c.doctag_locks[_doc_tag], c.work, + our_saxpy(&c.layer1_size, &c.doctags_lockf[_doc_tag % c.doctags_lockf_len], c.work, &ONE, &c.doctag_vectors[_doc_tag * c.layer1_size], &ONE) if c.learn_words: for m in range(j, k): if m == i: continue else: - our_saxpy(&c.layer1_size, &c.word_locks[c.indexes[m]], c.work, &ONE, + our_saxpy(&c.layer1_size, &c.words_lockf[c.indexes[m] % c.words_lockf_len], c.work, &ONE, &c.word_vectors[c.indexes[m] * c.layer1_size], &ONE) total_documents += 1 @@ -378,9 +380,9 @@ def d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, - _expected_words, work, neu1, docvecs_count, word_vectors=None, word_locks=None, + _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, - doctag_locks=None): + doctags_lockf=None): """Train distributed memory model ("PV-DM") by training on a corpus file, using a concatenation of the context window word vectors (rather than a sum or average). This might be slower since the input at each batch will be significantly larger. @@ -408,13 +410,13 @@ def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_ Whether or not the weights of the hidden layer will be updated. word_vectors : numpy.ndarray, optional The vector representation for each word in the vocabulary. If None, these will be retrieved from the model. - word_locks : numpy.ndarray, optional - A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates, - a value of 1 allows to update word-vectors. + words_lockf : numpy.ndarray, optional + EXPERIMENTAL. A learning lock factor for each word-vector, value 0.0 completely blocks updates, a value + of 1.0 allows normal updates to word-vectors. doctag_vectors : numpy.ndarray, optional Vector representations of the tags. If None, these will be retrieved from the model. - doctag_locks : numpy.ndarray, optional - The lock factors for each tag, same as `word_locks`, but for document-vectors. + doctags_lockf : numpy.ndarray, optional + EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors. Returns ------- @@ -446,8 +448,8 @@ def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_ init_d2v_config( &c, model, _alpha, learn_doctags, learn_words, learn_hidden, train_words=False, - work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks, docvecs_count=docvecs_count) + work=work, neu1=neu1, word_vectors=word_vectors, words_lockf=words_lockf, + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf, docvecs_count=docvecs_count) # release GIL & train on the full corpus, document by document with nogil: @@ -503,11 +505,11 @@ def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_ c.indexes[i], c.alpha, c.work, c.layer1_size, c.vector_size, c.learn_hidden) if c.learn_doctags and _doc_tag < c.docvecs_count: - our_saxpy(&c.vector_size, &c.doctag_locks[_doc_tag], &c.work[m * c.vector_size], + our_saxpy(&c.vector_size, &c.doctags_lockf[_doc_tag % c.doctags_lockf_len], &c.work[m * c.vector_size], &ONE, &c.doctag_vectors[_doc_tag * c.vector_size], &ONE) if c.learn_words: for m in range(2 * c.window): - our_saxpy(&c.vector_size, &c.word_locks[c.window_indexes[m]], &c.work[(c.doctag_len + m) * c.vector_size], + our_saxpy(&c.vector_size, &c.words_lockf[c.window_indexes[m] % c.words_lockf_len], &c.work[(c.doctag_len + m) * c.vector_size], &ONE, &c.word_vectors[c.window_indexes[m] * c.vector_size], &ONE) total_documents += 1 diff --git a/gensim/models/doc2vec_inner.pxd b/gensim/models/doc2vec_inner.pxd index c70dc616cc..77da86f449 100644 --- a/gensim/models/doc2vec_inner.pxd +++ b/gensim/models/doc2vec_inner.pxd @@ -26,8 +26,10 @@ cdef struct Doc2VecConfig: REAL_t *word_vectors REAL_t *doctag_vectors - REAL_t *word_locks - REAL_t *doctag_locks + REAL_t *words_lockf + np.uint32_t words_lockf_len + REAL_t *doctags_lockf + np.uint32_t doctags_lockf_len REAL_t *work REAL_t *neu1 REAL_t alpha @@ -54,14 +56,15 @@ cdef void fast_document_dbow_hs( const np.uint32_t *word_point, const np.uint8_t *word_code, const int codelen, REAL_t *context_vectors, REAL_t *syn1, const int size, const np.uint32_t context_index, const REAL_t alpha, REAL_t *work, int learn_context, int learn_hidden, - REAL_t *context_locks) nogil + REAL_t *contexts_lockf, const np.uint32_t contexts_lockf_len) nogil cdef unsigned long long fast_document_dbow_neg( const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, REAL_t *context_vectors, REAL_t *syn1neg, const int size, const np.uint32_t word_index, const np.uint32_t context_index, const REAL_t alpha, REAL_t *work, - unsigned long long next_random, int learn_context, int learn_hidden, REAL_t *context_locks) nogil + unsigned long long next_random, int learn_context, int learn_hidden, REAL_t *contexts_lockf, + const np.uint32_t contexts_lockf_len) nogil cdef void fast_document_dm_hs( @@ -89,4 +92,4 @@ cdef unsigned long long fast_document_dmc_neg( cdef init_d2v_config(Doc2VecConfig *c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=*, work=*, - neu1=*, word_vectors=*, word_locks=*, doctag_vectors=*, doctag_locks=*, docvecs_count=*) + neu1=*, word_vectors=*, words_lockf=*, doctag_vectors=*, doctags_lockf=*, docvecs_count=*) diff --git a/gensim/models/doc2vec_inner.pyx b/gensim/models/doc2vec_inner.pyx index e462110960..fe7ccf98ac 100644 --- a/gensim/models/doc2vec_inner.pyx +++ b/gensim/models/doc2vec_inner.pyx @@ -38,7 +38,7 @@ cdef void fast_document_dbow_hs( const np.uint32_t *word_point, const np.uint8_t *word_code, const int codelen, REAL_t *context_vectors, REAL_t *syn1, const int size, const np.uint32_t context_index, const REAL_t alpha, REAL_t *work, int learn_context, int learn_hidden, - REAL_t *context_locks) nogil: + REAL_t *contexts_lockf, const np.uint32_t contexts_lockf_len) nogil: cdef long long a, b cdef long long row1 = context_index * size, row2 @@ -56,14 +56,16 @@ cdef void fast_document_dbow_hs( if learn_hidden: our_saxpy(&size, &g, &context_vectors[row1], &ONE, &syn1[row2], &ONE) if learn_context: - our_saxpy(&size, &context_locks[context_index], work, &ONE, &context_vectors[row1], &ONE) + our_saxpy(&size, &contexts_lockf[context_index % contexts_lockf_len], + work, &ONE, &context_vectors[row1], &ONE) cdef unsigned long long fast_document_dbow_neg( const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, REAL_t *context_vectors, REAL_t *syn1neg, const int size, const np.uint32_t word_index, const np.uint32_t context_index, const REAL_t alpha, REAL_t *work, - unsigned long long next_random, int learn_context, int learn_hidden, REAL_t *context_locks) nogil: + unsigned long long next_random, int learn_context, int learn_hidden, REAL_t *contexts_lockf, + const np.uint32_t contexts_lockf_len) nogil: cdef long long a cdef long long row1 = context_index * size, row2 @@ -94,7 +96,8 @@ cdef unsigned long long fast_document_dbow_neg( if learn_hidden: our_saxpy(&size, &g, &context_vectors[row1], &ONE, &syn1neg[row2], &ONE) if learn_context: - our_saxpy(&size, &context_locks[context_index], work, &ONE, &context_vectors[row1], &ONE) + our_saxpy(&size, &contexts_lockf[context_index % contexts_lockf_len], + work, &ONE, &context_vectors[row1], &ONE) return next_random @@ -221,8 +224,8 @@ cdef unsigned long long fast_document_dmc_neg( cdef init_d2v_config(Doc2VecConfig *c, model, alpha, learn_doctags, learn_words, learn_hidden, - train_words=False, work=None, neu1=None, word_vectors=None, word_locks=None, doctag_vectors=None, - doctag_locks=None, docvecs_count=0): + train_words=False, work=None, neu1=None, word_vectors=None, words_lockf=None, + doctag_vectors=None, doctags_lockf=None, docvecs_count=0): c[0].hs = model.hs c[0].negative = model.negative c[0].sample = (model.sample != 0) @@ -250,12 +253,14 @@ cdef init_d2v_config(Doc2VecConfig *c, model, alpha, learn_doctags, learn_words, if doctag_vectors is None: doctag_vectors = model.dv.vectors c[0].doctag_vectors = (np.PyArray_DATA(doctag_vectors)) - if word_locks is None: - word_locks = model.wv.vectors_lockf - c[0].word_locks = (np.PyArray_DATA(word_locks)) - if doctag_locks is None: - doctag_locks = model.dv.vectors_lockf - c[0].doctag_locks = (np.PyArray_DATA(doctag_locks)) + if words_lockf is None: + words_lockf = model.wv.vectors_lockf + c[0].words_lockf = (np.PyArray_DATA(words_lockf)) + c[0].words_lockf_len = len(words_lockf) + if doctags_lockf is None: + doctags_lockf = model.dv.vectors_lockf + c[0].doctags_lockf = (np.PyArray_DATA(doctags_lockf)) + c[0].doctags_lockf_len = len(doctags_lockf) if c[0].hs: c[0].syn1 = (np.PyArray_DATA(model.syn1)) @@ -279,7 +284,7 @@ cdef init_d2v_config(Doc2VecConfig *c, model, alpha, learn_doctags, learn_words, 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): + word_vectors=None, words_lockf=None, doctag_vectors=None, doctags_lockf=None): """Update distributed bag of words model ("PV-DBOW") by training on a single document. Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train` and @@ -310,13 +315,13 @@ def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, Whether or not the weights of the hidden layer will be updated. word_vectors : numpy.ndarray, optional The vector representation for each word in the vocabulary. If None, these will be retrieved from the model. - word_locks : numpy.ndarray, optional - A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates, - a value of 1 allows to update word-vectors. + words_lockf : numpy.ndarray, optional + EXPERIMENTAL. A learning lock factor for each word-vector; value 0.0 completely blocks updates, a value + of 1.0 allows normal updates to word-vectors. doctag_vectors : numpy.ndarray, optional Vector representations of the tags. If None, these will be retrieved from the model. - doctag_locks : numpy.ndarray, optional - The lock factors for each tag, same as `word_locks`, but for document-vectors. + doctags_lockf : numpy.ndarray, optional + EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors. Returns ------- @@ -331,8 +336,8 @@ def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, cdef np.uint32_t *vocab_sample_ints init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=train_words, work=work, - neu1=None, word_vectors=word_vectors, word_locks=word_locks, - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) + neu1=None, word_vectors=word_vectors, words_lockf=words_lockf, + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf) c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) if c.sample: vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) @@ -383,31 +388,33 @@ def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, if c.hs: # we reuse the DBOW function, as it is equivalent to skip-gram for this purpose fast_document_dbow_hs(c.points[i], c.codes[i], c.codelens[i], c.word_vectors, c.syn1, c.layer1_size, - c.indexes[j], c.alpha, c.work, c.learn_words, c.learn_hidden, c.word_locks) + c.indexes[j], c.alpha, c.work, c.learn_words, c.learn_hidden, c.words_lockf, + c.words_lockf_len) if c.negative: # we reuse the DBOW function, as it is equivalent to skip-gram for this purpose c.next_random = fast_document_dbow_neg(c.negative, c.cum_table, c.cum_table_len, c.word_vectors, c.syn1neg, c.layer1_size, c.indexes[i], c.indexes[j], c.alpha, c.work, c.next_random, c.learn_words, - c.learn_hidden, c.word_locks) + c.learn_hidden, c.words_lockf, c.words_lockf_len) # docvec-training for j in range(c.doctag_len): if c.hs: fast_document_dbow_hs(c.points[i], c.codes[i], c.codelens[i], c.doctag_vectors, c.syn1, c.layer1_size, - c.doctag_indexes[j], c.alpha, c.work, c.learn_doctags, c.learn_hidden, c.doctag_locks) + c.doctag_indexes[j], c.alpha, c.work, c.learn_doctags, c.learn_hidden, c.doctags_lockf, + c.doctags_lockf_len) if c.negative: c.next_random = fast_document_dbow_neg(c.negative, c.cum_table, c.cum_table_len, c.doctag_vectors, c.syn1neg, c.layer1_size, c.indexes[i], c.doctag_indexes[j], c.alpha, c.work, c.next_random, c.learn_doctags, - c.learn_hidden, c.doctag_locks) + c.learn_hidden, c.doctags_lockf, c.doctags_lockf_len) return result 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): + word_vectors=None, words_lockf=None, doctag_vectors=None, doctags_lockf=None): """Update distributed memory model ("PV-DM") by training on a single document. 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. @@ -439,13 +446,13 @@ def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=N Whether or not the weights of the hidden layer will be updated. word_vectors : numpy.ndarray, optional The vector representation for each word in the vocabulary. If None, these will be retrieved from the model. - word_locks : numpy.ndarray, optional - A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates, - a value of 1 allows to update word-vectors. + words_lockf : numpy.ndarray, optional + EXPERIMENTAL. A learning lock factor for each word-vector; value 0.0 completely blocks updates, a value + of 1.0 allows normal updates to word-vectors. doctag_vectors : numpy.ndarray, optional Vector representations of the tags. If None, these will be retrieved from the model. - doctag_locks : numpy.ndarray, optional - The lock factors for each tag, same as `word_locks`, but for document-vectors. + doctags_lockf : numpy.ndarray, optional + EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors. Returns ------- @@ -461,8 +468,8 @@ def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=N cdef np.uint32_t *vocab_sample_ints init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=False, - work=work, neu1=neu1, word_vectors=word_vectors, word_locks=word_locks, - doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) + work=work, neu1=neu1, word_vectors=word_vectors, words_lockf=words_lockf, + doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf) c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) if c.sample: vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) @@ -537,14 +544,14 @@ def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=N # apply accumulated error in work if c.learn_doctags: for m in range(c.doctag_len): - our_saxpy(&c.layer1_size, &c.doctag_locks[c.doctag_indexes[m]], c.work, + our_saxpy(&c.layer1_size, &c.doctags_lockf[c.doctag_indexes[m] % c.doctags_lockf_len], c.work, &ONE, &c.doctag_vectors[c.doctag_indexes[m] * c.layer1_size], &ONE) if c.learn_words: for m in range(j, k): if m == i: continue else: - our_saxpy(&c.layer1_size, &c.word_locks[c.indexes[m]], c.work, &ONE, + our_saxpy(&c.layer1_size, &c.words_lockf[c.indexes[m] % c.doctags_lockf_len], c.work, &ONE, &c.word_vectors[c.indexes[m] * c.layer1_size], &ONE) return result @@ -552,10 +559,10 @@ def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=N 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). - This might be slower since the input at each batch will be significantly larger. + word_vectors=None, words_lockf=None, doctag_vectors=None, doctags_lockf=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). + This will be slower since the input at each batch will be significantly larger. Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train` and :meth:`~gensim.models.doc2vec.Doc2Vec.infer_vector`. @@ -584,13 +591,13 @@ def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, Whether or not the weights of the hidden layer will be updated. word_vectors : numpy.ndarray, optional The vector representation for each word in the vocabulary. If None, these will be retrieved from the model. - word_locks : numpy.ndarray, optional - A learning lock factor for each weight in the hidden layer for words, value 0 completely blocks updates, - a value of 1 allows to update word-vectors. + words_lockf : numpy.ndarray, optional + EXPERIMENTAL. A learning lock factor for each word-vector, value 0.0 completely blocks updates, a value + of 1.0 allows normal updates to word-vectors. doctag_vectors : numpy.ndarray, optional Vector representations of the tags. If None, these will be retrieved from the model. - doctag_locks : numpy.ndarray, optional - The lock factors for each tag, same as `word_locks`, but for document-vectors. + doctags_lockf : numpy.ndarray, optional + EXPERIMENTAL. The lock factors for each tag, same as `words_lockf`, but for document-vectors. Returns ------- @@ -605,7 +612,7 @@ def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, cdef np.uint32_t *vocab_sample_ints init_d2v_config(&c, model, alpha, learn_doctags, learn_words, learn_hidden, train_words=False, work=work, neu1=neu1, - word_vectors=word_vectors, word_locks=word_locks, doctag_vectors=doctag_vectors, doctag_locks=doctag_locks) + word_vectors=word_vectors, words_lockf=words_lockf, doctag_vectors=doctag_vectors, doctags_lockf=doctags_lockf) c.doctag_len = min(MAX_DOCUMENT_LEN, len(doctag_indexes)) if c.sample: vocab_sample_ints = np.PyArray_DATA(model.wv.expandos['sample_int']) @@ -675,11 +682,11 @@ def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, if c.learn_doctags: for m in range(c.doctag_len): - our_saxpy(&c.vector_size, &c.doctag_locks[c.doctag_indexes[m]], &c.work[m * c.vector_size], + our_saxpy(&c.vector_size, &c.doctags_lockf[c.doctag_indexes[m] % c.doctags_lockf_len], &c.work[m * c.vector_size], &ONE, &c.doctag_vectors[c.doctag_indexes[m] * c.vector_size], &ONE) if c.learn_words: for m in range(2 * c.window): - our_saxpy(&c.vector_size, &c.word_locks[c.window_indexes[m]], &c.work[(c.doctag_len + m) * c.vector_size], + our_saxpy(&c.vector_size, &c.words_lockf[c.window_indexes[m] % c.words_lockf_len], &c.work[(c.doctag_len + m) * c.vector_size], &ONE, &c.word_vectors[c.window_indexes[m] * c.vector_size], &ONE) return result diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 8c7f0015f4..e0d0e32d1e 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -489,12 +489,16 @@ def prepare_weights(self, update=False): super(FastText, self).prepare_weights(update=update) if not update: self.wv.init_ngrams_weights(self.seed) - self.wv.vectors_vocab_lockf = ones(len(self.wv.vectors_vocab), dtype=REAL) - self.wv.vectors_ngrams_lockf = ones(len(self.wv.vectors_ngrams), dtype=REAL) + # EXPERIMENTAL lockf feature; create minimal no-op lockf arrays (1 element of 1.0) + # advanced users should directly resize/adjust as necessary + self.wv.vectors_vocab_lockf = ones(1, dtype=REAL) + self.wv.vectors_ngrams_lockf = ones(1, dtype=REAL) else: self.wv.update_ngrams_weights(self.seed, self.old_vocab_len) - self.wv.vectors_vocab_lockf = _pad_ones(self.wv.vectors_vocab_lockf, len(self.wv.vectors_vocab)) - self.wv.vectors_ngrams_lockf = _pad_ones(self.wv.vectors_ngrams_lockf, len(self.wv.vectors_ngrams)) + # EXPERIMENTAL lockf feature; create minimal no-op lockf arrays (1 element of 1.0) + # advanced users should directly resize/adjust as necessary + self.wv.vectors_vocab_lockf = ones(1, dtype=REAL) + self.wv.vectors_ngrams_lockf = ones(1, dtype=REAL) def init_post_load(self, hidden_output): num_vectors = len(self.wv.vectors) @@ -504,8 +508,10 @@ def init_post_load(self, hidden_output): assert num_vectors > 0, 'expected num_vectors to be initialized already' assert vocab_size > 0, 'expected vocab_size to be initialized already' - self.wv.vectors_ngrams_lockf = ones(len(self.wv.vectors_ngrams), dtype=REAL) - self.wv.vectors_vocab_lockf = ones(len(self.wv.vectors_vocab), dtype=REAL) + # EXPERIMENTAL lockf feature; create minimal no-op lockf arrays (1 element of 1.0) + # advanced users should directly resize/adjust as necessary + self.wv.vectors_ngrams_lockf = ones(1, dtype=REAL) + self.wv.vectors_vocab_lockf = ones(1, dtype=REAL) if self.hs: self.syn1 = hidden_output @@ -869,15 +875,15 @@ def load(cls, *args, **kwargs): if not hasattr(model.wv, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'): # TODO: try trainables-location - model.wv.vectors_vocab_lockf = ones(len(model.wv.vectors_vocab), dtype=REAL) + model.wv.vectors_vocab_lockf = ones(1, dtype=REAL) if not hasattr(model, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'): # TODO: try trainables-location - model.wv.vectors_ngrams_lockf = ones(len(model.wv.vectors_ngrams), dtype=REAL) + model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL) # fixup mistakenly overdimensioned gensim-3.x lockf arrays if len(model.wv.vectors_vocab_lockf.shape) > 1: - model.wv.vectors_vocab_lockf = model.wv.vectors_vocab_lockf[:, 0] + model.wv.vectors_vocab_lockf = ones(1, dtype=REAL) if len(model.wv.vectors_ngrams_lockf.shape) > 1: - model.wv.vectors_ngrams_lockf = model.wv.vectors_ngrams_lockf[:, 0] + model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL) if not hasattr(model, 'bucket'): model.bucket = model.wv.bucket @@ -1489,7 +1495,8 @@ def _rollback_optimization(kv): kv.vectors_ngrams = _unpack(kv.vectors_ngrams, kv.bucket, kv.hash2index) if hasattr(kv, 'vectors_ngrams_lockf'): - kv.vectors_ngrams_lockf = _unpack(kv.vectors_ngrams_lockf, kv.bucket, kv.hash2index, fill=1.0) + # just clobber with no-op lockf array: vanishingly unlikely this experimental feature used in old files + kv.vectors_ngrams_lockf = ones(1, dtype=REAL) # # We have replaced num_ngram_vectors with a property and deprecated it. diff --git a/gensim/models/fasttext_inner.pxd b/gensim/models/fasttext_inner.pxd index 927f1b0978..fe66e3c545 100644 --- a/gensim/models/fasttext_inner.pxd +++ b/gensim/models/fasttext_inner.pxd @@ -59,13 +59,17 @@ cdef struct FastTextConfig: REAL_t *syn0_ngrams # + # EXPERIMENTAL # The arrays below selectively enable/disable training for specific vocab - # terms and ngrams. If word_locks_vocab[i] is 0, training is disabled; - # if it is 1, training is enabled. - # - REAL_t *word_locks_vocab - REAL_t *word_locks_ngrams - + # terms and ngrams. If vocab_locks[i] is 0.0, training is disabled; + # if it is 1.0, normal training is enabled. Other values scale updates. + # If undersized for vocab/ngrams, (index % actual_size) is used - + # so that a minimal single-element `lockf` can apply to all slots. + # + REAL_t *vocab_lockf + np.uint32_t vocab_lockf_len + REAL_t *ngrams_lockf + np.uint32_t ngrams_lockf_len # # Working memory. These are typically large enough to hold a single # vector each. diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index e00a2128f4..140e01da9e 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -113,8 +113,10 @@ cdef void fasttext_fast_sentence_sg_neg(FastTextConfig *c, int i, int j) nogil: REAL_t *work = c.work REAL_t *l1 = c.neu1 unsigned long long next_random = c.next_random - REAL_t *word_locks_vocab = c.word_locks_vocab - REAL_t *word_locks_ngrams = c.word_locks_ngrams + REAL_t *vocab_lockf = c.vocab_lockf + np.uint32_t vocab_lockf_len = c.vocab_lockf_len + REAL_t *ngrams_lockf = c.ngrams_lockf + np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len cdef long long row1 = word2_index * size, row2 cdef unsigned long long modulo = 281474976710655ULL @@ -156,9 +158,10 @@ cdef void fasttext_fast_sentence_sg_neg(FastTextConfig *c, int i, int j) nogil: g = (label - f) * alpha our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE) our_saxpy(&size, &g, l1, &ONE, &syn1neg[row2], &ONE) - our_saxpy(&size, &word_locks_vocab[word2_index], work, &ONE, &syn0_vocab[row1], &ONE) + our_saxpy(&size, &vocab_lockf[word2_index % vocab_lockf_len], work, &ONE, &syn0_vocab[row1], &ONE) for d in range(subwords_len): - our_saxpy(&size, &word_locks_ngrams[subwords_index[d]], work, &ONE, &syn0_ngrams[subwords_index[d]*size], &ONE) + our_saxpy(&size, &ngrams_lockf[subwords_index[d] % ngrams_lockf_len], + work, &ONE, &syn0_ngrams[subwords_index[d]*size], &ONE) c.next_random = next_random @@ -192,8 +195,10 @@ cdef void fasttext_fast_sentence_sg_hs(FastTextConfig *c, int i, int j) nogil: REAL_t alpha = c.alpha REAL_t *work = c.work REAL_t *l1 = c.neu1 - REAL_t *word_locks_vocab = c.word_locks_vocab - REAL_t *word_locks_ngrams = c.word_locks_ngrams + REAL_t *vocab_lockf = c.vocab_lockf + np.uint32_t vocab_lockf_len = c.vocab_lockf_len + REAL_t *ngrams_lockf = c.ngrams_lockf + np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len # # b : long long @@ -240,10 +245,10 @@ cdef void fasttext_fast_sentence_sg_hs(FastTextConfig *c, int i, int j) nogil: our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE) our_saxpy(&size, &g, l1, &ONE, &syn1[row2], &ONE) - our_saxpy(&size, &word_locks_vocab[word2_index], work, &ONE, &syn0_vocab[row1], &ONE) + our_saxpy(&size, &vocab_lockf[word2_index % vocab_lockf_len], work, &ONE, &syn0_vocab[row1], &ONE) for d in range(subwords_len): row2 = subwords_index[d] * size - our_saxpy(&size, &word_locks_ngrams[subwords_index[d]], work, &ONE, &syn0_ngrams[row2], &ONE) + our_saxpy(&size, &ngrams_lockf[subwords_index[d] % ngrams_lockf_len], work, &ONE, &syn0_ngrams[row2], &ONE) cdef void fasttext_fast_sentence_cbow_neg(FastTextConfig *c, int i, int j, int k) nogil: @@ -283,8 +288,10 @@ cdef void fasttext_fast_sentence_cbow_neg(FastTextConfig *c, int i, int j, int k REAL_t *work = c.work int cbow_mean = c.cbow_mean unsigned long long next_random = c.next_random - REAL_t *word_locks_vocab = c.word_locks_vocab - REAL_t *word_locks_ngrams = c.word_locks_ngrams + REAL_t *vocab_lockf = c.vocab_lockf + np.uint32_t vocab_lockf_len = c.vocab_lockf_len + REAL_t *ngrams_lockf = c.ngrams_lockf + np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len cdef long long row2 cdef unsigned long long modulo = 281474976710655ULL @@ -342,9 +349,9 @@ cdef void fasttext_fast_sentence_cbow_neg(FastTextConfig *c, int i, int j, int k for m in range(j,k): if m == i: continue - our_saxpy(&size, &word_locks_vocab[indexes[m]], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE) + our_saxpy(&size, &vocab_lockf[indexes[m] % vocab_lockf_len], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE) for d in range(subwords_idx_len[m]): - our_saxpy(&size, &word_locks_ngrams[subwords_idx[m][d]], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE) + our_saxpy(&size, &ngrams_lockf[subwords_idx[m][d] % ngrams_lockf_len], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE) c.next_random = next_random @@ -380,8 +387,10 @@ cdef void fasttext_fast_sentence_cbow_hs(FastTextConfig *c, int i, int j, int k) REAL_t alpha = c.alpha REAL_t *work = c.work int cbow_mean = c.cbow_mean - REAL_t *word_locks_vocab = c.word_locks_vocab - REAL_t *word_locks_ngrams = c.word_locks_ngrams + REAL_t *vocab_lockf = c.vocab_lockf + np.uint32_t vocab_lockf_len = c.vocab_lockf_len + REAL_t *ngrams_lockf = c.ngrams_lockf + np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len cdef long long b cdef long long row2 @@ -421,9 +430,9 @@ cdef void fasttext_fast_sentence_cbow_hs(FastTextConfig *c, int i, int j, int k) for m in range(j,k): if m == i: continue - our_saxpy(&size, &word_locks_vocab[indexes[m]], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE) + our_saxpy(&size, &vocab_lockf[indexes[m] % vocab_lockf_len], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE) for d in range(subwords_idx_len[m]): - our_saxpy(&size, &word_locks_ngrams[subwords_idx[m][d]], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE) + our_saxpy(&size, &ngrams_lockf[subwords_idx[m][d] % ngrams_lockf_len], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE) cdef void init_ft_config(FastTextConfig *c, model, alpha, _work, _neu1): @@ -453,9 +462,13 @@ cdef void init_ft_config(FastTextConfig *c, model, alpha, _work, _neu1): c.workers = model.workers c.syn0_vocab = (np.PyArray_DATA(model.wv.vectors_vocab)) - c.word_locks_vocab = (np.PyArray_DATA(model.wv.vectors_vocab_lockf)) c.syn0_ngrams = (np.PyArray_DATA(model.wv.vectors_ngrams)) - c.word_locks_ngrams = (np.PyArray_DATA(model.wv.vectors_ngrams_lockf)) + + # EXPERIMENTAL lockf scaled suppression/enablement of training + c.vocab_lockf = (np.PyArray_DATA(model.wv.vectors_vocab_lockf)) + c.vocab_lockf_len = len(model.wv.vectors_vocab_lockf) + c.ngrams_lockf = (np.PyArray_DATA(model.wv.vectors_ngrams_lockf)) + c.ngrams_lockf_len = len(model.wv.vectors_ngrams_lockf) c.alpha = alpha c.size = model.wv.vector_size diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index e9a55dac31..f48ec91de0 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -1540,7 +1540,7 @@ def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='ut if word in self.key_to_index: overlap_count += 1 self.vectors[self.get_index(word)] = weights - self.wv.vectors_lockf[self.get_index(word)] = lockf # lock-factor: 0.0=no changes + self.vectors_lockf[self.get_index(word)] = lockf # lock-factor: 0.0=no changes else: for line_no, line in enumerate(fin): parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ") @@ -1550,7 +1550,7 @@ def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='ut if word in self.key_to_index: overlap_count += 1 self.vectors[self.get_index(word)] = weights - self.wv.vectors_lockf[self.get_index(word)] = lockf # lock-factor: 0.0=no changes + self.vectors_lockf[self.get_index(word)] = lockf # lock-factor: 0.0=no changes logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.vectors.shape, fname) def get_keras_embedding(self, train_embeddings=False): diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 98061d39cb..11649a21c3 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -778,7 +778,7 @@ def reset_weights(self): if self.negative: self.syn1neg = np.zeros((len(self.wv), self.layer1_size), dtype=REAL) - self.wv.vectors_lockf = np.ones(len(self.wv), dtype=REAL) # zeros suppress learning + self.wv.vectors_lockf = np.ones(1, dtype=REAL) # 0.0 values suppress word-backprop-updates; 1.0 allows def update_weights(self): """Copy all the existing weights, and reset the weights for the newly added vocabulary.""" @@ -802,7 +802,7 @@ def update_weights(self): self.wv.norms = None # do not suppress learning for already learned words - self.wv.vectors_lockf = np.ones(len(self.wv), dtype=REAL) # zeros suppress learning + self.wv.vectors_lockf = np.ones(1, dtype=REAL) # 0.0 values suppress word-backprop-updates; 1.0 allows def _do_train_epoch(self, corpus_file, thread_id, offset, cython_vocab, thread_private_mem, cur_epoch, total_examples=None, total_words=None, **kwargs): @@ -1838,7 +1838,7 @@ def load(cls, *args, rethrow=False, **kwargs): if not hasattr(model, 'corpus_total_words'): model.corpus_total_words = None if not hasattr(model.wv, 'vectors_lockf') and hasattr(model.wv, 'vectors'): - model.wv.vectors_lockf = getattr(model, 'vectors_lockf', np.ones(len(model.wv.vectors), dtype=REAL)) + model.wv.vectors_lockf = getattr(model, 'vectors_lockf', np.ones(1, dtype=REAL)) if not hasattr(model, 'random'): model.random = np.random.RandomState(model.seed) if not hasattr(model, 'train_count'): diff --git a/gensim/models/word2vec_corpusfile.pyx b/gensim/models/word2vec_corpusfile.pyx index e75f250099..467b6a2d45 100644 --- a/gensim/models/word2vec_corpusfile.pyx +++ b/gensim/models/word2vec_corpusfile.pyx @@ -330,11 +330,13 @@ def train_epoch_sg(model, corpus_file, offset, _cython_vocab, _cur_epoch, _expec if c.hs: w2v_fast_sentence_sg_hs( c.points[i], c.codes[i], c.codelens[i], c.syn0, c.syn1, c.size, c.indexes[j], - c.alpha, c.work, c.word_locks, c.compute_loss, &c.running_training_loss) + c.alpha, c.work, c.words_lockf, c.words_lockf_len, c.compute_loss, + &c.running_training_loss) if c.negative: c.next_random = w2v_fast_sentence_sg_neg( c.negative, c.cum_table, c.cum_table_len, c.syn0, c.syn1neg, c.size, - c.indexes[i], c.indexes[j], c.alpha, c.work, c.next_random, c.word_locks, + c.indexes[i], c.indexes[j], c.alpha, c.work, c.next_random, + c.words_lockf, c.words_lockf_len, c.compute_loss, &c.running_training_loss) total_sentences += sentences.size() @@ -425,13 +427,15 @@ def train_epoch_cbow(model, corpus_file, offset, _cython_vocab, _cur_epoch, _exp if c.hs: w2v_fast_sentence_cbow_hs( c.points[i], c.codes[i], c.codelens, c.neu1, c.syn0, c.syn1, c.size, c.indexes, c.alpha, - c.work, i, j, k, c.cbow_mean, c.word_locks, c.compute_loss, &c.running_training_loss) + c.work, i, j, k, c.cbow_mean, c.words_lockf, c.words_lockf_len, c.compute_loss, + &c.running_training_loss) if c.negative: c.next_random = w2v_fast_sentence_cbow_neg( c.negative, c.cum_table, c.cum_table_len, c.codelens, c.neu1, c.syn0, c.syn1neg, c.size, c.indexes, c.alpha, c.work, i, j, k, c.cbow_mean, - c.next_random, c.word_locks, c.compute_loss, &c.running_training_loss) + c.next_random, c.words_lockf, c.words_lockf_len, c.compute_loss, + &c.running_training_loss) total_sentences += sentences.size() total_effective_words += effective_words diff --git a/gensim/models/word2vec_inner.pxd b/gensim/models/word2vec_inner.pxd index 67dfbb5770..d5ca66c49c 100644 --- a/gensim/models/word2vec_inner.pxd +++ b/gensim/models/word2vec_inner.pxd @@ -53,7 +53,8 @@ cdef struct Word2VecConfig: REAL_t running_training_loss, alpha REAL_t *syn0 - REAL_t *word_locks + REAL_t *words_lockf + np.uint32_t words_lockf_len REAL_t *work REAL_t *neu1 @@ -94,32 +95,32 @@ cdef unsigned long long random_int32(unsigned long long *next_random) nogil cdef void w2v_fast_sentence_sg_hs( const np.uint32_t *word_point, const np.uint8_t *word_code, const int codelen, REAL_t *syn0, REAL_t *syn1, const int size, - const np.uint32_t word2_index, const REAL_t alpha, REAL_t *work, REAL_t *word_locks, - const int _compute_loss, REAL_t *_running_training_loss_param) nogil + const np.uint32_t word2_index, const REAL_t alpha, REAL_t *work, REAL_t *words_lockf, + const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil cdef unsigned long long w2v_fast_sentence_sg_neg( const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, REAL_t *syn0, REAL_t *syn1neg, const int size, const np.uint32_t word_index, const np.uint32_t word2_index, const REAL_t alpha, REAL_t *work, - unsigned long long next_random, REAL_t *word_locks, - const int _compute_loss, REAL_t *_running_training_loss_param) nogil + unsigned long long next_random, REAL_t *words_lockf, + const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil cdef void w2v_fast_sentence_cbow_hs( const np.uint32_t *word_point, const np.uint8_t *word_code, int codelens[MAX_SENTENCE_LEN], REAL_t *neu1, REAL_t *syn0, REAL_t *syn1, const int size, const np.uint32_t indexes[MAX_SENTENCE_LEN], const REAL_t alpha, REAL_t *work, - int i, int j, int k, int cbow_mean, REAL_t *word_locks, - const int _compute_loss, REAL_t *_running_training_loss_param) nogil + int i, int j, int k, int cbow_mean, REAL_t *words_lockf, + const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil cdef unsigned long long w2v_fast_sentence_cbow_neg( const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, int codelens[MAX_SENTENCE_LEN], REAL_t *neu1, REAL_t *syn0, REAL_t *syn1neg, const int size, const np.uint32_t indexes[MAX_SENTENCE_LEN], const REAL_t alpha, REAL_t *work, - int i, int j, int k, int cbow_mean, unsigned long long next_random, REAL_t *word_locks, - const int _compute_loss, REAL_t *_running_training_loss_param) nogil + int i, int j, int k, int cbow_mean, unsigned long long next_random, REAL_t *words_lockf, + const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil cdef init_w2v_config(Word2VecConfig *c, model, alpha, compute_loss, _work, _neu1=*) diff --git a/gensim/models/word2vec_inner.pyx b/gensim/models/word2vec_inner.pyx index 63095470be..4a8ff40051 100755 --- a/gensim/models/word2vec_inner.pyx +++ b/gensim/models/word2vec_inner.pyx @@ -74,8 +74,8 @@ cdef void our_saxpy_noblas(const int *N, const float *alpha, const float *X, con cdef void w2v_fast_sentence_sg_hs( const np.uint32_t *word_point, const np.uint8_t *word_code, const int codelen, REAL_t *syn0, REAL_t *syn1, const int size, - const np.uint32_t word2_index, const REAL_t alpha, REAL_t *work, REAL_t *word_locks, - const int _compute_loss, REAL_t *_running_training_loss_param) nogil: + const np.uint32_t word2_index, const REAL_t alpha, REAL_t *work, REAL_t *words_lockf, + const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil: """Train on a single effective word from the current batch, using the Skip-Gram model. In this model we are using a given word to predict a context word (a word that is @@ -102,7 +102,7 @@ cdef void w2v_fast_sentence_sg_hs( Learning rate. work Private working memory for each worker. - word_locks + words_lockf Lock factors for each word. A value of 0 will block training. _compute_loss Whether or not the loss should be computed at this step. @@ -135,7 +135,7 @@ cdef void w2v_fast_sentence_sg_hs( our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE) our_saxpy(&size, &g, &syn0[row1], &ONE, &syn1[row2], &ONE) - our_saxpy(&size, &word_locks[word2_index], work, &ONE, &syn0[row1], &ONE) + our_saxpy(&size, &words_lockf[word2_index % lockf_len], work, &ONE, &syn0[row1], &ONE) # to support random draws from negative-sampling cum_table @@ -160,8 +160,8 @@ cdef unsigned long long w2v_fast_sentence_sg_neg( const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, REAL_t *syn0, REAL_t *syn1neg, const int size, const np.uint32_t word_index, const np.uint32_t word2_index, const REAL_t alpha, REAL_t *work, - unsigned long long next_random, REAL_t *word_locks, - const int _compute_loss, REAL_t *_running_training_loss_param) nogil: + unsigned long long next_random, REAL_t *words_lockf, + const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil: """Train on a single effective word from the current batch, using the Skip-Gram model. In this model we are using a given word to predict a context word (a word that is @@ -193,7 +193,7 @@ cdef unsigned long long w2v_fast_sentence_sg_neg( Private working memory for each worker. next_random Seed to produce the index for the next word to be randomly sampled. - word_locks + words_lockf Lock factors for each word. A value of 0 will block training. _compute_loss Whether or not the loss should be computed at this step. @@ -242,7 +242,7 @@ cdef unsigned long long w2v_fast_sentence_sg_neg( our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE) our_saxpy(&size, &g, &syn0[row1], &ONE, &syn1neg[row2], &ONE) - our_saxpy(&size, &word_locks[word2_index], work, &ONE, &syn0[row1], &ONE) + our_saxpy(&size, &words_lockf[word2_index % lockf_len], work, &ONE, &syn0[row1], &ONE) return next_random @@ -251,7 +251,7 @@ cdef void w2v_fast_sentence_cbow_hs( const np.uint32_t *word_point, const np.uint8_t *word_code, int codelens[MAX_SENTENCE_LEN], REAL_t *neu1, REAL_t *syn0, REAL_t *syn1, const int size, const np.uint32_t indexes[MAX_SENTENCE_LEN], const REAL_t alpha, REAL_t *work, - int i, int j, int k, int cbow_mean, REAL_t *word_locks, + int i, int j, int k, int cbow_mean, REAL_t *words_lockf, const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil: """Train on a single effective word from the current batch, using the CBOW method. @@ -289,7 +289,7 @@ cdef void w2v_fast_sentence_cbow_hs( Index of the word at the end of the context window. cbow_mean If 0, use the sum of the context word vectors as the prediction. If 1, use the mean. - word_locks + words_lockf Lock factors for each word. A value of 0 will block training. _compute_loss Whether or not the loss should be computed at this step. @@ -342,15 +342,15 @@ cdef void w2v_fast_sentence_cbow_hs( if m == i: continue else: - our_saxpy(&size, &word_locks[indexes[m]], work, &ONE, &syn0[indexes[m] * size], &ONE) + our_saxpy(&size, &words_lockf[indexes[m] % lockf_len], work, &ONE, &syn0[indexes[m] * size], &ONE) cdef unsigned long long w2v_fast_sentence_cbow_neg( const int negative, np.uint32_t *cum_table, unsigned long long cum_table_len, int codelens[MAX_SENTENCE_LEN], REAL_t *neu1, REAL_t *syn0, REAL_t *syn1neg, const int size, const np.uint32_t indexes[MAX_SENTENCE_LEN], const REAL_t alpha, REAL_t *work, - int i, int j, int k, int cbow_mean, unsigned long long next_random, REAL_t *word_locks, - const int _compute_loss, REAL_t *_running_training_loss_param) nogil: + int i, int j, int k, int cbow_mean, unsigned long long next_random, REAL_t *words_lockf, + const np.uint32_t lockf_len, const int _compute_loss, REAL_t *_running_training_loss_param) nogil: """Train on a single effective word from the current batch, using the CBOW method. Using this method we train the trainable neural network by attempting to predict a @@ -392,7 +392,7 @@ cdef unsigned long long w2v_fast_sentence_cbow_neg( If 0, use the sum of the context word vectors as the prediction. If 1, use the mean. next_random Seed for the drawing the predicted word for the next iteration of the same routine. - word_locks + words_lockf Lock factors for each word. A value of 0 will block training. _compute_loss Whether or not the loss should be computed at this step. @@ -459,7 +459,7 @@ cdef unsigned long long w2v_fast_sentence_cbow_neg( if m == i: continue else: - our_saxpy(&size, &word_locks[indexes[m]], work, &ONE, &syn0[indexes[m]*size], &ONE) + our_saxpy(&size, &words_lockf[indexes[m] % lockf_len], work, &ONE, &syn0[indexes[m]*size], &ONE) return next_random @@ -476,7 +476,8 @@ cdef init_w2v_config(Word2VecConfig *c, model, alpha, compute_loss, _work, _neu1 c[0].running_training_loss = model.running_training_loss c[0].syn0 = (np.PyArray_DATA(model.wv.vectors)) - c[0].word_locks = (np.PyArray_DATA(model.wv.vectors_lockf)) + c[0].words_lockf = (np.PyArray_DATA(model.wv.vectors_lockf)) + c[0].words_lockf_len = len(model.wv.vectors_lockf) c[0].alpha = alpha c[0].size = model.wv.vector_size @@ -584,9 +585,9 @@ def train_batch_sg(model, sentences, alpha, _work, compute_loss): if j == i: continue if c.hs: - w2v_fast_sentence_sg_hs(c.points[i], c.codes[i], c.codelens[i], c.syn0, c.syn1, c.size, c.indexes[j], c.alpha, c.work, c.word_locks, c.compute_loss, &c.running_training_loss) + w2v_fast_sentence_sg_hs(c.points[i], c.codes[i], c.codelens[i], c.syn0, c.syn1, c.size, c.indexes[j], c.alpha, c.work, c.words_lockf, c.words_lockf_len, c.compute_loss, &c.running_training_loss) if c.negative: - c.next_random = w2v_fast_sentence_sg_neg(c.negative, c.cum_table, c.cum_table_len, c.syn0, c.syn1neg, c.size, c.indexes[i], c.indexes[j], c.alpha, c.work, c.next_random, c.word_locks, c.compute_loss, &c.running_training_loss) + c.next_random = w2v_fast_sentence_sg_neg(c.negative, c.cum_table, c.cum_table_len, c.syn0, c.syn1neg, c.size, c.indexes[i], c.indexes[j], c.alpha, c.work, c.next_random, c.words_lockf, c.words_lockf_len, c.compute_loss, &c.running_training_loss) model.running_training_loss = c.running_training_loss return effective_words @@ -677,9 +678,9 @@ def train_batch_cbow(model, sentences, alpha, _work, _neu1, compute_loss): if k > idx_end: k = idx_end if c.hs: - w2v_fast_sentence_cbow_hs(c.points[i], c.codes[i], c.codelens, c.neu1, c.syn0, c.syn1, c.size, c.indexes, c.alpha, c.work, i, j, k, c.cbow_mean, c.word_locks, c.compute_loss, &c.running_training_loss) + w2v_fast_sentence_cbow_hs(c.points[i], c.codes[i], c.codelens, c.neu1, c.syn0, c.syn1, c.size, c.indexes, c.alpha, c.work, i, j, k, c.cbow_mean, c.words_lockf, c.words_lockf_len, c.compute_loss, &c.running_training_loss) if c.negative: - c.next_random = w2v_fast_sentence_cbow_neg(c.negative, c.cum_table, c.cum_table_len, c.codelens, c.neu1, c.syn0, c.syn1neg, c.size, c.indexes, c.alpha, c.work, i, j, k, c.cbow_mean, c.next_random, c.word_locks, c.compute_loss, &c.running_training_loss) + c.next_random = w2v_fast_sentence_cbow_neg(c.negative, c.cum_table, c.cum_table_len, c.codelens, c.neu1, c.syn0, c.syn1neg, c.size, c.indexes, c.alpha, c.work, i, j, k, c.cbow_mean, c.next_random, c.words_lockf, c.words_lockf_len, c.compute_loss, &c.running_training_loss) model.running_training_loss = c.running_training_loss return effective_words diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index e588422822..af20c071e0 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -191,8 +191,8 @@ def model_structural_sanity(self, model): self.assertEqual(model.wv.vectors.shape, (len(model.wv), model.vector_size)) self.assertEqual(model.wv.vectors_vocab.shape, (len(model.wv), model.vector_size)) self.assertEqual(model.wv.vectors_ngrams.shape, (model.wv.bucket, model.vector_size)) - self.assertEqual(len(model.wv.vectors_ngrams_lockf), len(model.wv.vectors_ngrams)) - self.assertEqual(len(model.wv.vectors_vocab_lockf), len(model.wv.index_to_key)) + self.assertLessEqual(len(model.wv.vectors_ngrams_lockf), len(model.wv.vectors_ngrams)) + self.assertLessEqual(len(model.wv.vectors_vocab_lockf), len(model.wv.index_to_key)) self.assertTrue(np.isfinite(model.wv.vectors_ngrams).all(), "NaN in ngrams") self.assertTrue(np.isfinite(model.wv.vectors_vocab).all(), "NaN in vectors_vocab") if model.negative: diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 3e1a1c7935..84a61c6c30 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -548,6 +548,8 @@ def testLocking(self): # remember two vectors locked0 = np.copy(model.wv.vectors[0]) unlocked1 = np.copy(model.wv.vectors[1]) + # alocate a full lockf array (not just default single val for all) + model.wv.vectors_lockf = np.ones(len(model.wv), dtype=np.float32) # lock the vector in slot 0 against change model.wv.vectors_lockf[0] = 0.0 @@ -839,7 +841,7 @@ def testLoadOldModel(self): self.assertTrue(len(model.wv) == 12) self.assertTrue(len(model.wv.index2word) == 12) self.assertTrue(model.syn1neg.shape == (len(model.wv), model.wv.vector_size)) - self.assertTrue(model.wv.vectors_lockf.shape == (12,)) + self.assertTrue(len(model.wv.vectors_lockf.shape) > 0) self.assertTrue(model.cum_table.shape == (12,)) self.onlineSanity(model, trained_model=True) @@ -854,7 +856,7 @@ def testLoadOldModelSeparates(self): self.assertTrue(len(model.wv) == 12) self.assertTrue(len(model.wv.index2word) == 12) self.assertTrue(model.syn1neg.shape == (len(model.wv), model.wv.vector_size)) - self.assertTrue(model.wv.vectors_lockf.shape == (12,)) + self.assertTrue(len(model.wv.vectors_lockf.shape) > 0) self.assertTrue(model.cum_table.shape == (12,)) self.onlineSanity(model, trained_model=True) From fe3ae3160f855c6000befb6140055af6dca45be2 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 26 May 2020 13:01:02 -0700 Subject: [PATCH 34/60] improve FT comment --- gensim/models/fasttext.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index e0d0e32d1e..9e09edc54d 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -1449,12 +1449,10 @@ def adjust_vectors(self): def recalc_word_ngram_buckets(self): """ - Performs a common operation for FastText weight initialization and - updates: scan the vocabulary, calculate ngrams and their hashes, keep - track of new ngrams, the buckets that each word relates to via its - ngrams, etc. + Scans the vocabulary, calculates ngrams and their hashes, and cache the + list of ngrams for each known word. - TODO: evaluate if this is even necessary, compared to just recalculating + TODO: evaluate if precaching even necessary, compared to recalculating as needed """ if self.bucket == 0: self.buckets_word = [np.array([], dtype=np.uint32)] * len(self.index_to_key) From d503205385be9c4809f65f7a78ea7f78ca36eedc Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 26 May 2020 13:26:16 -0700 Subject: [PATCH 35/60] rm deprecated/unneded init_sims calls --- gensim/test/test_similarities.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/gensim/test/test_similarities.py b/gensim/test/test_similarities.py index 4657191d31..3556438655 100644 --- a/gensim/test/test_similarities.py +++ b/gensim/test/test_similarities.py @@ -553,7 +553,6 @@ def setUp(self): def testWord2Vec(self): model = word2vec.Word2Vec(texts, min_count=1) - model.wv.init_sims() index = self.indexer(model, 10) self.assertVectorIsSimilarToItself(model.wv, index) @@ -572,7 +571,6 @@ def __iter__(self): yield line.lower().strip().split() model = FastText(LeeReader(datapath('lee.cor')), bucket=5000) - model.wv.init_sims() index = self.indexer(model, 10) self.assertVectorIsSimilarToItself(model.wv, index) @@ -655,7 +653,6 @@ def setUp(self): from gensim.similarities.index import AnnoyIndexer self.model = doc2vec.Doc2Vec(sentences, min_count=1) - self.model.dv.init_sims() self.index = AnnoyIndexer(self.model, 300) self.vector = self.model.dv.vectors_norm[0] @@ -716,7 +713,6 @@ def setUp(self): def test_word2vec(self): model = word2vec.Word2Vec(texts, min_count=1) - model.wv.init_sims() index = self.indexer(model) self.assertVectorIsSimilarToItself(model.wv, index) @@ -735,7 +731,6 @@ def __iter__(self): yield line.lower().strip().split() model = FastText(LeeReader(datapath('lee.cor')), bucket=5000) - model.wv.init_sims() index = self.indexer(model) self.assertVectorIsSimilarToItself(model.wv, index) @@ -807,7 +802,6 @@ def setUp(self): from gensim.similarities.nmslib import NmslibIndexer self.model = doc2vec.Doc2Vec(sentences, min_count=1) - self.model.dv.init_sims() self.index = NmslibIndexer(self.model) self.vector = self.model.dv.vectors_norm[0] From 411473ba934830cedd3474be177d01ea1db6ba5b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Radim=20=C5=98eh=C5=AF=C5=99ek?= Date: Mon, 6 Jul 2020 14:10:58 +0200 Subject: [PATCH 36/60] fixes to code style --- gensim/corpora/sharded_corpus.py | 36 ++++++------- gensim/models/doc2vec.py | 2 - gensim/models/doc2vec_corpusfile.pyx | 78 ++++++++++++++++----------- gensim/models/doc2vec_inner.pyx | 6 +-- gensim/models/fasttext.py | 80 +++++++++++----------------- gensim/models/fasttext_inner.pyx | 2 +- gensim/models/keyedvectors.py | 40 +++++++------- gensim/models/word2vec.py | 2 +- gensim/models/word2vec_inner.pxd | 2 +- gensim/models/word2vec_inner.pyx | 19 ++++++- gensim/similarities/termsim.py | 6 ++- setup.py | 11 ++-- 12 files changed, 146 insertions(+), 138 deletions(-) diff --git a/gensim/corpora/sharded_corpus.py b/gensim/corpora/sharded_corpus.py index 4b30b4ec7b..030bba2352 100644 --- a/gensim/corpora/sharded_corpus.py +++ b/gensim/corpora/sharded_corpus.py @@ -22,10 +22,10 @@ import logging import os import math -import numpy -import scipy.sparse as sparse import time +import numpy +import scipy.sparse as sparse from six.moves import range import gensim @@ -263,9 +263,7 @@ def init_shards(self, output_prefix, corpus, shardsize=4096, dtype=_default_dtyp is_corpus, corpus = gensim.utils.is_corpus(corpus) if not is_corpus: - raise ValueError( - "Cannot initialize shards without a corpus to read from! (Got corpus type: {0})".format(type(corpus)) - ) + raise ValueError("Cannot initialize shards without a corpus to read from! Got corpus type: %s" % type(corpus)) proposed_dim = self._guess_n_features(corpus) if proposed_dim != self.dim: @@ -360,7 +358,7 @@ def load_shard(self, n): filename = self._shard_name(n) if not os.path.isfile(filename): - raise ValueError('Attempting to load nonexistent shard no. {0}'.format(n)) + raise ValueError('Attempting to load nonexistent shard no. %s' % n) shard = gensim.utils.unpickle(filename) self.current_shard = shard @@ -387,11 +385,9 @@ def shard_by_offset(self, offset): """ k = int(offset / self.shardsize) if offset >= self.n_docs: - raise ValueError('Too high offset specified ({0}), available ' - 'docs: {1}'.format(offset, self.n_docs)) + raise ValueError('Too high offset specified (%s), available docs: %s' % (offset, self.n_docs)) if offset < 0: - raise ValueError('Negative offset {0} currently not' - ' supported.'.format(offset)) + raise ValueError('Negative offset %s currently not supported.' % offset) return k def in_current(self, offset): @@ -440,9 +436,8 @@ def resize_shards(self, shardsize): if new_stop > self.n_docs: # Sanity check assert new_shard_idx == n_new_shards - 1, \ - 'Shard no. {0} that ends at {1} over last document' \ - ' ({2}) is not the last projected shard ({3})???' \ - ''.format(new_shard_idx, new_stop, self.n_docs, n_new_shards) + 'Shard no. %r that ends at %r over last document (%r) is not the last projected shard (%r)' % ( + new_shard_idx, new_stop, self.n_docs, n_new_shards) new_stop = self.n_docs new_shard = self[new_start:new_stop] @@ -524,9 +519,8 @@ def _guess_n_features(self, corpus): else: if not self.dim: raise TypeError( - "Couldn't find number of features, refusing to guess " - "(dimension set to {0}, type of corpus: {1})." - .format(self.dim, type(corpus)) + "Couldn't find number of features, refusing to guess (dimension set to %s, type of corpus: %s)." % ( + self.dim, type(corpus)) ) else: logger.warning("Couldn't find number of features, trusting supplied dimension (%d)", self.dim) @@ -591,7 +585,7 @@ def __getitem__(self, offset): start = offset.start stop = offset.stop if stop > self.n_docs: - raise IndexError('Requested slice offset {0} out of range ({1} docs)'.format(stop, self.n_docs)) + raise IndexError('Requested slice offset %s out of range (%s docs)' % (stop, self.n_docs)) # - get range of shards over which to iterate first_shard = self.shard_by_offset(start) @@ -687,8 +681,8 @@ def __add_to_slice(self, s_result, result_start, result_stop, start, stop): """ if (result_stop - result_start) != (stop - start): raise ValueError( - 'Result start/stop range different than stop/start range ({0} - {1} vs. {2} - {3})' - .format(result_start, result_stop, start, stop) + 'Result start/stop range different than stop/start range (%s - %s vs. %s - %s)' % ( + result_start, result_stop, start, stop) ) # Dense data: just copy using numpy's slice notation @@ -702,8 +696,8 @@ def __add_to_slice(self, s_result, result_start, result_stop, start, stop): else: if s_result.shape != (result_start, self.dim): raise ValueError( - 'Assuption about sparse s_result shape invalid: {0} expected rows, {1} real rows.' - .format(result_start, s_result.shape[0]) + 'Assuption about sparse s_result shape invalid: %s expected rows, %s real rows.' % ( + result_start, s_result.shape[0]) ) tmp_matrix = self.current_shard[start:stop] diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py index 620ee95a43..1a55ad9b5f 100644 --- a/gensim/models/doc2vec.py +++ b/gensim/models/doc2vec.py @@ -1067,12 +1067,10 @@ def similarity_unseen_docs(self, doc_words1, doc_words2, alpha=None, min_alpha=N class Doc2VecVocab(utils.SaveLoad): """Obsolete class retained for now as load-compatibility state capture""" - pass class Doc2VecTrainables(utils.SaveLoad): """Obsolete class retained for now as load-compatibility state capture""" - pass class TaggedBrownCorpus(object): diff --git a/gensim/models/doc2vec_corpusfile.pyx b/gensim/models/doc2vec_corpusfile.pyx index 4b50dd0125..5b8cbeabff 100644 --- a/gensim/models/doc2vec_corpusfile.pyx +++ b/gensim/models/doc2vec_corpusfile.pyx @@ -54,11 +54,13 @@ cdef int ONE = 1 cdef REAL_t ONEF = 1.0 -cdef void prepare_c_structures_for_batch(vector[string] &doc_words, int sample, int hs, int window, long long *total_words, - int *effective_words, unsigned long long *next_random, cvocab_t *vocab, - np.uint32_t *indexes, int *codelens, np.uint8_t **codes, np.uint32_t **points, - np.uint32_t *reduced_windows, int *document_len, int train_words, - int docvecs_count, int doc_tag) nogil: +cdef void prepare_c_structures_for_batch( + vector[string] &doc_words, int sample, int hs, int window, long long *total_words, + int *effective_words, unsigned long long *next_random, cvocab_t *vocab, + np.uint32_t *indexes, int *codelens, np.uint8_t **codes, np.uint32_t **points, + np.uint32_t *reduced_windows, int *document_len, int train_words, + int docvecs_count, int doc_tag, + ) nogil: cdef VocabItem predict_word cdef string token cdef int i = 0 @@ -92,10 +94,12 @@ cdef void prepare_c_structures_for_batch(vector[string] &doc_words, int sample, effective_words[0] += 1 -def d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, - _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, - train_words=False, learn_doctags=True, learn_words=True, learn_hidden=True, - doctag_vectors=None, doctags_lockf=None): +def d2v_train_epoch_dbow( + model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, + _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, + train_words=False, learn_doctags=True, learn_words=True, learn_hidden=True, + doctag_vectors=None, doctags_lockf=None, + ): """Train distributed bag of words model ("PV-DBOW") by training on a corpus file. Called internally from :meth:`~gensim.models.doc2vec.Doc2Vec.train`. @@ -231,9 +235,11 @@ def d2v_train_epoch_dbow(model, corpus_file, offset, start_doctag, _cython_vocab return total_documents, total_effective_words, total_words -def d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, - _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, - learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, doctags_lockf=None): +def d2v_train_epoch_dm( + model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, + _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, + learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, doctags_lockf=None, + ): """Train distributed memory model ("PV-DM") by training on a corpus file. 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. @@ -359,30 +365,35 @@ def d2v_train_epoch_dm(model, corpus_file, offset, start_doctag, _cython_vocab, sscal(&c.layer1_size, &inv_count, c.work, &ONE) # (does this need BLAS-variants like saxpy?) # apply accumulated error in work if c.learn_doctags and _doc_tag < c.docvecs_count: - our_saxpy(&c.layer1_size, &c.doctags_lockf[_doc_tag % c.doctags_lockf_len], c.work, - &ONE, &c.doctag_vectors[_doc_tag * c.layer1_size], &ONE) + our_saxpy( + &c.layer1_size, &c.doctags_lockf[_doc_tag % c.doctags_lockf_len], c.work, + &ONE, &c.doctag_vectors[_doc_tag * c.layer1_size], &ONE) if c.learn_words: for m in range(j, k): if m == i: continue else: - our_saxpy(&c.layer1_size, &c.words_lockf[c.indexes[m] % c.words_lockf_len], c.work, &ONE, - &c.word_vectors[c.indexes[m] * c.layer1_size], &ONE) + our_saxpy( + &c.layer1_size, &c.words_lockf[c.indexes[m] % c.words_lockf_len], c.work, &ONE, + &c.word_vectors[c.indexes[m] * c.layer1_size], &ONE) total_documents += 1 total_effective_words += effective_words _doc_tag += 1 - c.alpha = get_next_alpha(start_alpha, end_alpha, total_documents, total_words, expected_examples, - expected_words, cur_epoch, num_epochs) + c.alpha = get_next_alpha( + start_alpha, end_alpha, total_documents, total_words, expected_examples, + expected_words, cur_epoch, num_epochs) return total_documents, total_effective_words, total_words -def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, - _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, - learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, - doctags_lockf=None): +def d2v_train_epoch_dm_concat( + model, corpus_file, offset, start_doctag, _cython_vocab, _cur_epoch, _expected_examples, + _expected_words, work, neu1, docvecs_count, word_vectors=None, words_lockf=None, + learn_doctags=True, learn_words=True, learn_hidden=True, doctag_vectors=None, + doctags_lockf=None, + ): """Train distributed memory model ("PV-DM") by training on a corpus file, using a concatenation of the context window word vectors (rather than a sum or average). This might be slower since the input at each batch will be significantly larger. @@ -477,8 +488,7 @@ def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_ # compose l1 & clear work if _doc_tag < c.docvecs_count: # doc vector(s) - memcpy(&c.neu1[0], &c.doctag_vectors[_doc_tag * c.vector_size], - c.vector_size * cython.sizeof(REAL_t)) + memcpy(&c.neu1[0], &c.doctag_vectors[_doc_tag * c.vector_size], c.vector_size * cython.sizeof(REAL_t)) n = 0 for m in range(j, k): # word vectors in window @@ -490,8 +500,9 @@ def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_ c.window_indexes[n] = c.indexes[m] n += 1 for m in range(2 * c.window): - memcpy(&c.neu1[(c.doctag_len + m) * c.vector_size], &c.word_vectors[c.window_indexes[m] * c.vector_size], - c.vector_size * cython.sizeof(REAL_t)) + memcpy( + &c.neu1[(c.doctag_len + m) * c.vector_size], &c.word_vectors[c.window_indexes[m] * c.vector_size], + c.vector_size * cython.sizeof(REAL_t)) memset(c.work, 0, c.layer1_size * cython.sizeof(REAL_t)) # work to accumulate l1 error if c.hs: @@ -505,19 +516,22 @@ def d2v_train_epoch_dm_concat(model, corpus_file, offset, start_doctag, _cython_ c.indexes[i], c.alpha, c.work, c.layer1_size, c.vector_size, c.learn_hidden) if c.learn_doctags and _doc_tag < c.docvecs_count: - our_saxpy(&c.vector_size, &c.doctags_lockf[_doc_tag % c.doctags_lockf_len], &c.work[m * c.vector_size], - &ONE, &c.doctag_vectors[_doc_tag * c.vector_size], &ONE) + our_saxpy( + &c.vector_size, &c.doctags_lockf[_doc_tag % c.doctags_lockf_len], &c.work[m * c.vector_size], + &ONE, &c.doctag_vectors[_doc_tag * c.vector_size], &ONE) if c.learn_words: for m in range(2 * c.window): - our_saxpy(&c.vector_size, &c.words_lockf[c.window_indexes[m] % c.words_lockf_len], &c.work[(c.doctag_len + m) * c.vector_size], - &ONE, &c.word_vectors[c.window_indexes[m] * c.vector_size], &ONE) + our_saxpy( + &c.vector_size, &c.words_lockf[c.window_indexes[m] % c.words_lockf_len], &c.work[(c.doctag_len + m) * c.vector_size], + &ONE, &c.word_vectors[c.window_indexes[m] * c.vector_size], &ONE) total_documents += 1 total_effective_words += effective_words _doc_tag += 1 - c.alpha = get_next_alpha(start_alpha, end_alpha, total_documents, total_words, expected_examples, - expected_words, cur_epoch, num_epochs) + c.alpha = get_next_alpha( + start_alpha, end_alpha, total_documents, total_words, expected_examples, + expected_words, cur_epoch, num_epochs) return total_documents, total_effective_words, total_words diff --git a/gensim/models/doc2vec_inner.pyx b/gensim/models/doc2vec_inner.pyx index fe7ccf98ac..23ede53c90 100644 --- a/gensim/models/doc2vec_inner.pyx +++ b/gensim/models/doc2vec_inner.pyx @@ -347,7 +347,7 @@ def train_document_dbow(model, doc_words, doctag_indexes, alpha, work=None, i = 0 for token in doc_words: - word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + word_index = model.wv.key_to_index.get(token, None) if word_index is None: # shrink document to leave out word continue # leaving i unchanged if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): @@ -480,7 +480,7 @@ def train_document_dm(model, doc_words, doctag_indexes, alpha, work=None, neu1=N i = 0 for token in doc_words: - word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + word_index = model.wv.key_to_index.get(token, None) if word_index is None: # shrink document to leave out word continue # leaving i unchanged if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): @@ -625,7 +625,7 @@ def train_document_dm_concat(model, doc_words, doctag_indexes, alpha, work=None, i = 0 for token in doc_words: - word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None + word_index = model.wv.key_to_index.get(token, None) if word_index is None: # shrink document to leave out word continue # leaving i unchanged if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index fed28123cf..d6314ea48a 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -277,14 +277,13 @@ import logging import os +from collections.abc import Iterable import numpy as np from numpy import ones, vstack, float32 as REAL import six -from collections.abc import Iterable import gensim.models._fasttext_bin - from gensim.models.word2vec import Word2Vec from gensim.models.keyedvectors import KeyedVectors from gensim import utils @@ -308,6 +307,7 @@ class FastText(Word2Vec): + def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, word_ngrams=1, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, @@ -501,7 +501,7 @@ def prepare_weights(self, update=False): self.wv.vectors_vocab_lockf = ones(1, dtype=REAL) self.wv.vectors_ngrams_lockf = ones(1, dtype=REAL) - def init_post_load(self, hidden_output): + def _init_post_load(self, hidden_output): num_vectors = len(self.wv.vectors) vocab_size = len(self.wv) vector_size = self.wv.vector_size @@ -606,6 +606,7 @@ def _clear_post_train(self): self.wv.adjust_vectors() # ensure composite-word vecs reflect latest training def estimate_memory(self, vocab_size=None, report=None): + """Estimate memory that will be needed to train a model, and print the estimates to log.""" vocab_size = vocab_size or len(self.wv) vec_size = self.vector_size * np.dtype(np.float32).itemsize l1_size = self.layer1_size * np.dtype(np.float32).itemsize @@ -624,13 +625,7 @@ def estimate_memory(self, vocab_size=None, report=None): buckets = set() num_ngrams = 0 for word in self.wv.key_to_index: - hashes = ft_ngram_hashes( - word, - self.wv.min_n, - self.wv.max_n, - self.bucket, - self.wv.compatible_hash - ) + hashes = ft_ngram_hashes(word, self.wv.min_n, self.wv.max_n, self.bucket, self.wv.compatible_hash) num_ngrams += len(hashes) buckets.update(hashes) num_buckets = len(buckets) @@ -639,14 +634,11 @@ def estimate_memory(self, vocab_size=None, report=None): # Only used during training, not stored with the model report['buckets_word'] = 48 * len(self.wv) + 8 * num_ngrams # FIXME: this looks confused -gojomo elif self.word_ngrams > 0: - logger.warn( - 'subword information is enabled, but no vocabulary could be found, estimated required memory might be ' - 'inaccurate!' - ) + logger.warning('subword information is enabled, but no vocabulary could be found, estimated required memory might be inaccurate!') report['total'] = sum(report.values()) logger.info( "estimated required memory for %i words, %i buckets and %i dimensions: %i bytes", - len(self.wv), num_buckets, self.vector_size, report['total'] + len(self.wv), num_buckets, self.vector_size, report['total'], ) return report @@ -848,8 +840,7 @@ def save(self, *args, **kwargs): Load :class:`~gensim.models.fasttext.FastText` model. """ - kwargs['ignore'] = kwargs.get( - 'ignore', []) + ['buckets_word', ] + kwargs['ignore'] = kwargs.get('ignore', []) + ['buckets_word', ] super(FastText, self).save(*args, **kwargs) @classmethod @@ -898,17 +889,16 @@ def load(cls, *args, **kwargs): class FastTextVocab(utils.SaveLoad): """This is a redundant class. It exists only to maintain backwards compatibility with older gensim versions.""" - pass class FastTextTrainables(utils.SaveLoad): """Obsolete class retained for backward-compatible load()s""" - pass def _pad_ones(m, new_len): """Pad array with additional entries filled with ones.""" - assert len(m) <= new_len, 'the new number of rows %i must be greater than old %i' % (new_len, len(m)) + if len(m) > new_len: + raise ValueError('the new number of rows %i must be greater than old %i' % (new_len, len(m))) new_arr = np.ones(new_len, dtype=REAL) new_arr[:len(m)] = m return new_arr @@ -1093,7 +1083,7 @@ def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): model.num_original_vectors = m.vectors_ngrams.shape[0] model.wv.init_post_load(m.vectors_ngrams) - model.init_post_load(m.hidden_output) + model._init_post_load(m.hidden_output) _check_model(model) @@ -1102,30 +1092,28 @@ def _load_fasttext_format(model_file, encoding='utf-8', full_model=True): def _check_model(m): - # - # These checks only make sense after everything has been completely initialized. - # - assert m.wv.vector_size == m.wv.vectors_ngrams.shape[1], ( - 'mismatch between vector size in model params ({}) and model vectors ({})' - .format(m.wv.vector_size, m.wv.vectors_ngrams) - ) + """Model sanity checks. Run after everything has been completely initialized.""" + if m.wv.vector_size != m.wv.vectors_ngrams.shape[1]: + raise ValueError( + 'mismatch between vector size in model params (%s) and model vectors (%s)' % (m.wv.vector_size, m.wv.vectors_ngrams) + ) if hasattr(m, 'syn1neg') and m.syn1neg is not None: - assert m.wv.vector_size == m.syn1neg.shape[1], ( - 'mismatch between vector size in model params ({}) and trainables ({})' - .format(m.wv.vector_size, m.wv.vectors_ngrams) - ) + if m.wv.vector_size != m.syn1neg.shape[1]: + raise ValueError( + 'mismatch between vector size in model params (%s) and trainables (%s)' % (m.wv.vector_size, m.wv.vectors_ngrams) + ) - assert len(m.wv) == m.nwords, ( - 'mismatch between final vocab size ({} words), ' - 'and expected number of words ({} words)'.format(len(m.wv), m.nwords) - ) + if len(m.wv) != m.nwords: + raise ValueError( + 'mismatch between final vocab size (%s words), and expected number of words (%s words)' % (len(m.wv), m.nwords) + ) if len(m.wv) != m.vocab_size: # expecting to log this warning only for pretrained french vector, wiki.fr logger.warning( "mismatch between final vocab size (%s words), and expected vocab size (%s words)", - len(m.wv), m.vocab_size + len(m.wv), m.vocab_size, ) @@ -1412,8 +1400,6 @@ def init_post_load(self, fb_vectors): and ngrams. This comes directly from the binary model. The order of the vectors must correspond to the indices in the vocabulary. - match_gensim : boolean, optional - No longer supported. """ vocab_words = len(self) @@ -1442,7 +1428,7 @@ def adjust_vectors(self): return self.vectors = self.vectors_vocab[:].copy() - for i, w in enumerate(self.index_to_key): + for i, _ in enumerate(self.index_to_key): ngram_buckets = self.buckets_word[i] for nh in ngram_buckets: self.vectors[i] += self.vectors_ngrams[nh] @@ -1450,11 +1436,10 @@ def adjust_vectors(self): def recalc_word_ngram_buckets(self): """ - Scans the vocabulary, calculates ngrams and their hashes, and cache the - list of ngrams for each known word. + Scan the vocabulary, calculate ngrams and their hashes, and cache the list of ngrams for each known word. - TODO: evaluate if precaching even necessary, compared to recalculating as needed """ + # FIXME: evaluate if precaching even necessary, compared to recalculating as needed if self.bucket == 0: self.buckets_word = [np.array([], dtype=np.uint32)] * len(self.index_to_key) return @@ -1463,14 +1448,13 @@ def recalc_word_ngram_buckets(self): for i, word in enumerate(self.index_to_key): self.buckets_word[i] = np.array( - ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash), - dtype=np.uint32, + ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash), dtype=np.uint32, ) def _pad_random(m, new_rows, rand): """Pad a matrix with additional rows filled with random values.""" - rows, columns = m.shape + _, columns = m.shape low, high = -1.0 / columns, 1.0 / columns suffix = rand.uniform(low, high, (new_rows, columns)).astype(REAL) return vstack([m, suffix]) @@ -1479,8 +1463,8 @@ def _pad_random(m, new_rows, rand): def _rollback_optimization(kv): """Undo the optimization that pruned buckets. - This unfortunate optimization saves memory and CPU cycles, but breaks - compatibility with Facebook's model by introducing divergent behavior + This unfortunate optimization saved memory and CPU cycles in pre-4.0 Gensim versions, + but broke compatibility with Facebook's model by introducing divergent behavior for OOV words. """ diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index 140e01da9e..bc3f4cf5ad 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -8,7 +8,7 @@ """Optimized Cython functions for training a :class:`~gensim.models.fasttext.FastText` model. The main entry point is :func:`~gensim.models.fasttext_inner.train_batch_any` -which may be called directly from Python code. +which may be called directly from Python code. Notes ----- diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index f48ec91de0..2ac068b632 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -256,7 +256,7 @@ def allocate_vecattrs(self, attrs=None, types=None): even if other properties (vectors array) hasn't yet been allocated or expanded. So this allocation targets that size. """ - # with no arguments, simply adjust sizes of existing + # With no arguments, simply adjust sizes of existing arrays. if attrs is None: attrs = list(self.expandos.keys()) types = [self.expandos[attr].dtype for attr in attrs] @@ -276,13 +276,13 @@ def allocate_vecattrs(self, attrs=None, types=None): prev_expando[0:min(prev_count, target_size), ] def set_vecattr(self, key, attr, val): - """ TODO """ + """ FIXME """ self.allocate_vecattrs(attrs=[attr], types=[type(val)]) index = self.get_index(key) self.expandos[attr][index] = val def get_vecattr(self, key, attr): - """ TODO """ + """ FIXME """ index = self.get_index(key) return self.expandos[attr][index] @@ -304,7 +304,7 @@ def resize_vectors(self): def randomly_initialize_vectors(self, indexes=None, seed=0): """Initialize vectors with low-magnitude random vectors, as is typical for pre-trained - Word2Vec and related models. + Word2Vec and related models. """ if indexes is None: @@ -379,9 +379,7 @@ def get_vector(self, key, use_norm=False): result.setflags(write=False) # disallow direct tampering that would invalidate `norms` etc return result - def word_vec(self, *args, **kwargs): - """Compatibility alias for get_vector()""" - return self.get_vector(*args, **kwargs) + word_vec = get_vector # Compatibility alias def add(self, keys, weights, extras=None, replace=False): """Append keys and their vectors in a manual way. @@ -394,8 +392,8 @@ def add(self, keys, weights, extras=None, replace=False): weights: list of numpy.ndarray or 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 keys which already exist in the map - if True - replace vectors, otherwise - keep old vectors. + Flag indicating whether to replace vectors for keys which already exist in the map. + If True - replace vectors, otherwise - keep old vectors. """ if isinstance(keys, KEY_TYPES): @@ -499,9 +497,9 @@ def fill_norms(self, force=False): """ Ensure per-vector norms are available. - (Any code which modifies vectors should ensure the - accompanying norms are recalculated, or 'None'-out - 'norms' to trigger full recalc later.) + Any code which modifies vectors should ensure the accompanying norms are + either recalculated or 'None', to trigger full recalc later. + """ if self.norms is None or force: self.norms = np.linalg.norm(self.vectors, axis=1) @@ -541,9 +539,10 @@ def vocab(self, value): @property def pseudovocab(self): - """ pseudodict providing pseudovocab objects + """A pseudodict providing pseudovocab objects. + + Not efficient, a temporary workaround for backward campatibility 'just in case' a .vocab use can't adapt. - not efficient, temp backcompat workaround 'just in case' a .vocab use can't adapt """ class Vocaboid(object): def __init__(self, kv, index): @@ -576,9 +575,9 @@ def sort_by_descending_frequency(self): self.index_to_key = list(np.array(self.index_to_key)[count_sorted_indexes]) self.allocate_vecattrs() for k in self.expandos: - self.expandos[k] = self.expandos[k][count_sorted_indexes] + self.expandos[k] = self.expandos[k][count_sorted_indexes] # uses numpy's "fancy indexing" to shuffle in one step if len(self.vectors): - logger.warning("sorting after vectors allocated expensive & error-prone") + logger.warning("sorting after vectors have been allocated is expensive & error-prone") self.vectors = self.vectors[count_sorted_indexes] for i, word in enumerate(self.index_to_key): self.key_to_index[word] = i @@ -627,7 +626,7 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip are searched for most-similar values. For example, restrict_vocab=10000 would only check the first 10000 key vectors in the vocabulary order. (This may be meaningful if you've sorted the vocabulary by descending frequency.) If - specified, overrides any values of clip_start or clip_end + specified, overrides any values of ``clip_start`` or ``clip_end``. Returns ------- @@ -881,8 +880,8 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): When `topn` is None, then similarities for all words are returned as a one-dimensional numpy array with the size of the vocabulary. - # TODO: Update to better match & share code with most_similar() """ + # FIXME: Update to better match & share code with most_similar() if isinstance(topn, Integral) and topn < 1: return [] @@ -1479,10 +1478,11 @@ def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', (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.) no_header : bool, optional - Default False means a usual word2ve-format file, with a 1st line declaring the count of - following vectors & number of dimensions. If True, the file is assumed lack a declaratory + Default False means a usual word2vec-format file, with a 1st line declaring the count of + following vectors & number of dimensions. If True, the file is assumed to lack a declaratory (vocab_size, vector_size) header and instead start with the 1st vector, and an extra reading-pass will be used to discover the number of vectors. Works only with `binary=False`. + Returns ------- :class:`~gensim.models.keyedvectors.KeyedVectors` diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py index 11649a21c3..a6523babdf 100755 --- a/gensim/models/word2vec.py +++ b/gensim/models/word2vec.py @@ -184,7 +184,7 @@ def __init__(self, sentences=None, corpus_file=None, vector_size=100, alpha=0.02 """Train, use and evaluate neural networks described in https://code.google.com/p/word2vec/. Once you're finished training a model (=no more updates, only querying) - store and use only the :class:`~gensim.models.keyedvectors.KeyedVectors` instance in `self.wv` + store and use only the :class:`~gensim.models.keyedvectors.KeyedVectors` instance in ``self.wv`` to reduce memory. The full model can be stored/loaded via its :meth:`~gensim.models.word2vec.Word2Vec.save` and diff --git a/gensim/models/word2vec_inner.pxd b/gensim/models/word2vec_inner.pxd index d5ca66c49c..82abad2f05 100644 --- a/gensim/models/word2vec_inner.pxd +++ b/gensim/models/word2vec_inner.pxd @@ -67,7 +67,7 @@ cdef struct Word2VecConfig: REAL_t *syn1 np.uint32_t *points[MAX_SENTENCE_LEN] np.uint8_t *codes[MAX_SENTENCE_LEN] - + # For negative sampling REAL_t *syn1neg np.uint32_t *cum_table diff --git a/gensim/models/word2vec_inner.pyx b/gensim/models/word2vec_inner.pyx index 4a8ff40051..50bfc803bd 100755 --- a/gensim/models/word2vec_inner.pyx +++ b/gensim/models/word2vec_inner.pyx @@ -730,7 +730,24 @@ def score_sentence_sg(model, sentence, _work): for token in sentence: word_index = model.wv.key_to_index[token] if token in model.wv.key_to_index else None if word_index is None: - continue # for score, should this be a default negative value? + # For score, should this be a default negative value? + # + # See comment by @gojomo at https://github.com/RaRe-Technologies/gensim/pull/2698/files#r445827846 : + # + # These 'score' functions are a long-ago contribution from @mataddy whose + # current function/utility is unclear. + # I've continued to apply mechanical updates to match other changes, and the code + # still compiles & passes the one (trivial, form-but-not-function) unit test. But it's an + # idiosyncratic technique, and only works for the non-default hs mode. Here, in lieu of the + # previous cryptic # should drop the comment, I've asked if for the purposes of this + # particular kind of 'scoring' (really, loss-tallying indicating how divergent this new + # text is from what the model learned during training), shouldn't completely missing + # words imply something very negative, as opposed to nothing-at-all? But probably, this + # functionality should be dropped. (And ultimately, a talented cleanup of the largely-broken + # loss-tallying functions might provide a cleaner window into this same measure of how + # well a text contrasts with model expectations - such as a way to report loss from a + # single invocation of one fo the inner train methods, without changing the model.) + continue c.indexes[i] = word_index c.codelens[i] = len(vocab_codes[word_index]) c.codes[i] = np.PyArray_DATA(vocab_codes[word_index]) diff --git a/gensim/similarities/termsim.py b/gensim/similarities/termsim.py index 545858c8b1..975d584660 100644 --- a/gensim/similarities/termsim.py +++ b/gensim/similarities/termsim.py @@ -115,8 +115,10 @@ def _shortest_uint_dtype(max_value): class WordEmbeddingSimilarityIndex(TermSimilarityIndex): """ - Computes cosine similarities between word embeddings and retrieves the closest word embeddings - by cosine similarity for a given word embedding. + Use objects of this class to: + + 1) Compute cosine similarities between word embeddings. + 2) Retrieve the closest word embeddings (by cosine similarity) to a given word embedding. Parameters ---------- diff --git a/setup.py b/setup.py index a07018fd39..025a2f5d55 100644 --- a/setup.py +++ b/setup.py @@ -41,7 +41,9 @@ def need_cython(): """Return True if we need Cython to translate any of the extensions. If the extensions have already been translated to C/C++, then we don't need - to install Cython and perform the translation.""" + to install Cython and perform the translation. + + """ expected = list(c_extensions.values()) + list(cpp_extensions.values()) return any([not os.path.isfile(f) for f in expected]) @@ -260,12 +262,13 @@ def run(self): distributed_env = ['Pyro4 >= 4.27'] -linux_testenv = [ +win_testenv = [ 'pytest', 'pytest-rerunfailures', 'mock', 'cython', 'nmslib', + 'pyemd', 'testfixtures', 'Morfessor==2.0.2a4', 'python-Levenshtein >= 0.10.2', @@ -275,12 +278,8 @@ def run(self): # See https://github.com/RaRe-Technologies/gensim/pull/2814 # 'tensorflow', # 'keras', - 'pyemd', # see below; keep as last until appveyor issue resolved ] -# temporarily remove pyemd to work around appveyor issues -win_testenv = linux_testenv[:-1] - # # This list partially duplicates requirements_docs.txt. # The main difference is that we don't include version pins here unless From 45fd5f670c0f59245671922fd71d5067834588b8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Radim=20=C5=98eh=C5=AF=C5=99ek?= Date: Mon, 6 Jul 2020 14:58:04 +0200 Subject: [PATCH 37/60] flake8: fix overlong lines --- gensim/corpora/sharded_corpus.py | 58 ++++++++++++++++++-------------- gensim/models/fasttext.py | 17 +++++++--- gensim/models/keyedvectors.py | 3 +- 3 files changed, 47 insertions(+), 31 deletions(-) diff --git a/gensim/corpora/sharded_corpus.py b/gensim/corpora/sharded_corpus.py index 030bba2352..c9ebf1841b 100644 --- a/gensim/corpora/sharded_corpus.py +++ b/gensim/corpora/sharded_corpus.py @@ -263,7 +263,7 @@ def init_shards(self, output_prefix, corpus, shardsize=4096, dtype=_default_dtyp is_corpus, corpus = gensim.utils.is_corpus(corpus) if not is_corpus: - raise ValueError("Cannot initialize shards without a corpus to read from! Got corpus type: %s" % type(corpus)) + raise ValueError("Cannot initialize shards without a corpus to read from! Corpus type: %s" % type(corpus)) proposed_dim = self._guess_n_features(corpus) if proposed_dim != self.dim: @@ -407,7 +407,7 @@ def in_next(self, offset): """ if self.current_shard_n == self.n_shards: return False # There's no next shard. - return (self.offsets[self.current_shard_n + 1] <= offset) and (offset < self.offsets[self.current_shard_n + 2]) + return self.offsets[self.current_shard_n + 1] <= offset and offset < self.offsets[self.current_shard_n + 2] def resize_shards(self, shardsize): """ @@ -461,9 +461,9 @@ def resize_shards(self, shardsize): for old_shard_n, old_shard_name in enumerate(old_shard_names): os.remove(old_shard_name) except Exception as e: - logger.error( - 'Exception occurred during old shard no. %d removal: %s.\nAttempting to at least move new shards in.', - old_shard_n, str(e) + logger.exception( + 'Error during old shard no. %d removal: %s.\nAttempting to at least move new shards in.', + old_shard_n, str(e), ) finally: # If something happens with cleaning up - try to at least get the @@ -474,7 +474,7 @@ def resize_shards(self, shardsize): # If something happens when we're in this stage, we're screwed. except Exception as e: logger.exception(e) - raise RuntimeError('Resizing completely failed for some reason. Sorry, dataset is probably ruined...') + raise RuntimeError('Resizing completely failed. Sorry, dataset is probably ruined...') finally: # Sets the new shard stats. self.n_shards = n_new_shards @@ -519,18 +519,18 @@ def _guess_n_features(self, corpus): else: if not self.dim: raise TypeError( - "Couldn't find number of features, refusing to guess (dimension set to %s, type of corpus: %s)." % ( - self.dim, type(corpus)) + "Couldn't find number of features, refusing to guess. Dimension: %s, corpus: %s)" % ( + self.dim, type(corpus), + ) ) - else: - logger.warning("Couldn't find number of features, trusting supplied dimension (%d)", self.dim) - n_features = self.dim + logger.warning("Couldn't find number of features, trusting supplied dimension (%d)", self.dim) + n_features = self.dim if self.dim and n_features != self.dim: logger.warning( "Discovered inconsistent dataset dim (%d) and feature count from corpus (%d). " "Coercing to dimension given by argument.", - self.dim, n_features + self.dim, n_features, ) return n_features @@ -668,21 +668,23 @@ def __getitem__(self, offset): def __add_to_slice(self, s_result, result_start, result_stop, start, stop): """ - Add the rows of the current shard from `start` to `stop` + Add rows of the current shard from `start` to `stop` into rows `result_start` to `result_stop` of `s_result`. - Operation is based on the self.sparse_serialize setting. If the shard + Operation is based on the ``self.sparse_serialize`` setting. If the shard contents are dense, then s_result is assumed to be an ndarray that already supports row indices `result_start:result_stop`. If the shard contents are sparse, assumes that s_result has `result_start` rows and we should add them up to `result_stop`. - Returns the resulting s_result. + Return the resulting ``s_result``. + """ if (result_stop - result_start) != (stop - start): raise ValueError( 'Result start/stop range different than stop/start range (%s - %s vs. %s - %s)' % ( - result_start, result_stop, start, stop) + result_start, result_stop, start, stop, + ) ) # Dense data: just copy using numpy's slice notation @@ -693,16 +695,16 @@ def __add_to_slice(self, s_result, result_start, result_stop, start, stop): # A bit more difficult, we're using a different structure to build the # result. - else: - if s_result.shape != (result_start, self.dim): - raise ValueError( - 'Assuption about sparse s_result shape invalid: %s expected rows, %s real rows.' % ( - result_start, s_result.shape[0]) + if s_result.shape != (result_start, self.dim): + raise ValueError( + 'Assuption about sparse s_result shape invalid: %s expected rows, %s real rows.' % ( + result_start, s_result.shape[0], ) + ) - tmp_matrix = self.current_shard[start:stop] - s_result = sparse.vstack([s_result, tmp_matrix]) - return s_result + tmp_matrix = self.current_shard[start:stop] + s_result = sparse.vstack([s_result, tmp_matrix]) + return s_result def _getitem_format(self, s_result): if self.sparse_serialization: @@ -811,5 +813,9 @@ def serialize(serializer, fname, corpus, id2word=None, index_fname=None, progres Ignore the parameters id2word, index_fname, progress_cnt, labels and metadata. They currently do nothing and are here only to - provide a compatible method signature with superclass.""" - serializer.save_corpus(fname, corpus, id2word=id2word, progress_cnt=progress_cnt, metadata=metadata, **kwargs) + provide a compatible method signature with superclass. + + """ + serializer.save_corpus( + fname, corpus, id2word=id2word, progress_cnt=progress_cnt, metadata=metadata, **kwargs, + ) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index d6314ea48a..cc83060166 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -634,7 +634,10 @@ def estimate_memory(self, vocab_size=None, report=None): # Only used during training, not stored with the model report['buckets_word'] = 48 * len(self.wv) + 8 * num_ngrams # FIXME: this looks confused -gojomo elif self.word_ngrams > 0: - logger.warning('subword information is enabled, but no vocabulary could be found, estimated required memory might be inaccurate!') + logger.warning( + 'Subword information is enabled, but no vocabulary could be found. ' + 'Estimated required memory might be inaccurate!', + ) report['total'] = sum(report.values()) logger.info( "estimated required memory for %i words, %i buckets and %i dimensions: %i bytes", @@ -1095,18 +1098,24 @@ def _check_model(m): """Model sanity checks. Run after everything has been completely initialized.""" if m.wv.vector_size != m.wv.vectors_ngrams.shape[1]: raise ValueError( - 'mismatch between vector size in model params (%s) and model vectors (%s)' % (m.wv.vector_size, m.wv.vectors_ngrams) + 'mismatch between vector size in model params (%s) and model vectors (%s)' % ( + m.wv.vector_size, m.wv.vectors_ngrams, + ) ) if hasattr(m, 'syn1neg') and m.syn1neg is not None: if m.wv.vector_size != m.syn1neg.shape[1]: raise ValueError( - 'mismatch between vector size in model params (%s) and trainables (%s)' % (m.wv.vector_size, m.wv.vectors_ngrams) + 'mismatch between vector size in model params (%s) and trainables (%s)' % ( + m.wv.vector_size, m.wv.vectors_ngrams, + ) ) if len(m.wv) != m.nwords: raise ValueError( - 'mismatch between final vocab size (%s words), and expected number of words (%s words)' % (len(m.wv), m.nwords) + 'mismatch between final vocab size (%s words), and expected number of words (%s words)' % ( + len(m.wv), m.nwords, + ) ) if len(m.wv) != m.vocab_size: diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 2ac068b632..6e1952955d 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -575,7 +575,8 @@ def sort_by_descending_frequency(self): self.index_to_key = list(np.array(self.index_to_key)[count_sorted_indexes]) self.allocate_vecattrs() for k in self.expandos: - self.expandos[k] = self.expandos[k][count_sorted_indexes] # uses numpy's "fancy indexing" to shuffle in one step + # Use numpy's "fancy indexing" to permutate the entire array in one step. + self.expandos[k] = self.expandos[k][count_sorted_indexes] if len(self.vectors): logger.warning("sorting after vectors have been allocated is expensive & error-prone") self.vectors = self.vectors[count_sorted_indexes] From 5764f8c02b53ca7cfe4af85246c7c264133a192a Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 10:01:15 -0700 Subject: [PATCH 38/60] rm stray merge error --- gensim/models/fasttext.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 872e0fadfe..29be765426 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -321,7 +321,6 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100 The model can be stored/loaded via its :meth:`~gensim.models.fasttext.FastText.save` and :meth:`~gensim.models.fasttext.FastText.load` methods, or loaded from a format compatible with the original Fasttext implementation via :func:`~gensim.models.fasttext.load_facebook_model`. - """ Parameters ---------- From e49ae4cd7e84843b30e19f2de6005722056d9d53 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 10:58:59 -0700 Subject: [PATCH 39/60] rm duplicated , old nonstandard hash workarounds --- gensim/models/_fasttext_bin.py | 2 +- gensim/models/fasttext.py | 103 +++--------------- gensim/models/fasttext_inner.pyx | 24 ---- .../test_data/compatible-hash-false.model | Bin 14826 -> 0 bytes gensim/test/test_fasttext.py | 48 +------- 5 files changed, 21 insertions(+), 156 deletions(-) delete mode 100644 gensim/test/test_data/compatible-hash-false.model diff --git a/gensim/models/_fasttext_bin.py b/gensim/models/_fasttext_bin.py index 64379a878c..26337d51eb 100644 --- a/gensim/models/_fasttext_bin.py +++ b/gensim/models/_fasttext_bin.py @@ -435,7 +435,7 @@ def _get_field_from_model(model, field): requested field name, fields are listed in the `_NEW_HEADER_FORMAT` list """ if field == 'bucket': - return model.bucket + return model.wv.bucket elif field == 'dim': return model.vector_size elif field == 'epoch': diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 29be765426..41e15396ea 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -299,7 +299,6 @@ MAX_WORDS_IN_BATCH, compute_ngrams, compute_ngrams_bytes, - ft_hash_broken, ft_hash_bytes, ) from gensim.models.fasttext_corpusfile import train_epoch_sg, train_epoch_cbow @@ -313,7 +312,7 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100 max_vocab_size=None, word_ngrams=1, sample=1e-3, seed=1, workers=3, min_alpha=0.0001, negative=5, ns_exponent=0.75, cbow_mean=1, hashfxn=hash, epochs=5, null_word=0, min_n=3, max_n=6, sorted_vocab=1, bucket=2000000, trim_rule=None, batch_words=MAX_WORDS_IN_BATCH, callbacks=(), - compatible_hash=True, max_final_vocab=None): + max_final_vocab=None): """Train, use and evaluate word representations learned using the method described in `Enriching Word Vectors with Subword Information `_, aka FastText. @@ -415,13 +414,6 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100 memory usage of the model. This option specifies the number of buckets used by the model. callbacks : :obj: `list` of :obj: `~gensim.models.callbacks.CallbackAny2Vec`, optional List of callbacks that need to be executed/run at specific stages during training. - - compatible_hash: bool, optional - By default, newer versions of Gensim's FastText use a hash function - that is 100% compatible with Facebook's FastText. - Older versions were not 100% compatible due to a bug. - To use the older, incompatible hash function, set this to False. - max_final_vocab : int, optional Limits the vocab to a target vocab size by automatically selecting ``min_count```. If the specified ``min_count`` is more than the @@ -472,8 +464,7 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100 if self.word_ngrams <= 1 and max_n == 0: bucket = 0 - self.wv = FastTextKeyedVectors(vector_size, min_n, max_n, bucket, compatible_hash) - self.bucket = bucket + self.wv = FastTextKeyedVectors(vector_size, min_n, max_n, bucket) self.wv.bucket = bucket super(FastText, self).__init__( @@ -618,7 +609,7 @@ def estimate_memory(self, vocab_size=None, report=None): report = report or {} report['vocab'] = len(self.wv) * (700 if self.hs else 500) report['syn0_vocab'] = len(self.wv) * vec_size - num_buckets = self.bucket + num_buckets = self.wv.bucket if self.hs: report['syn1'] = len(self.wv) * l1_size if self.negative: @@ -626,7 +617,7 @@ def estimate_memory(self, vocab_size=None, report=None): if self.word_ngrams > 0 and len(self.wv): num_buckets = num_ngrams = 0 - if self.bucket: + if self.wv.bucket: buckets = set() num_ngrams = 0 for word in self.wv.key_to_index: @@ -634,8 +625,7 @@ def estimate_memory(self, vocab_size=None, report=None): word, self.wv.min_n, self.wv.max_n, - self.bucket, - self.wv.compatible_hash + self.wv.bucket, ) num_ngrams += len(hashes) buckets.update(hashes) @@ -891,10 +881,8 @@ def load(cls, *args, **kwargs): model.wv.vectors_vocab_lockf = ones(1, dtype=REAL) if len(model.wv.vectors_ngrams_lockf.shape) > 1: model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL) - if not hasattr(model, 'bucket'): - model.bucket = model.wv.bucket - - _try_upgrade(model.wv) + if hasattr(model, 'bucket'): + del model.bucket # should only exist in one place: the wv subcomponent if not hasattr(model.wv, 'buckets_word') or not model.wv.buckets_word: model.wv.recalc_word_ngram_buckets() @@ -1173,7 +1161,7 @@ def save_facebook_model(model, path, encoding="utf-8", lr_update_rate=100, word_ class FastTextKeyedVectors(KeyedVectors): - def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): + def __init__(self, vector_size, min_n, max_n, bucket): """Vectors and vocab for :class:`~gensim.models.fasttext.FastText`. Implements significant parts of the FastText algorithm. For example, @@ -1185,12 +1173,6 @@ def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): Similar to a hashmap, this class keeps a fixed number of buckets, and maps all ngrams to buckets using a hash function. - This class also provides an abstraction over the hash functions used by - Gensim's FastText implementation over time. The hash function connects - ngrams to buckets. Originally, the hash function was broken and - incompatible with Facebook's implementation. The current hash is fully - compatible. - Parameters ---------- vector_size : int @@ -1201,9 +1183,6 @@ def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): The maximum number of characters in an ngram bucket : int The number of buckets. - compatible_hash : boolean - If True, uses the Facebook-compatible hash function instead of the - Gensim backwards-compatible hash function. Attributes ---------- @@ -1234,15 +1213,15 @@ def __init__(self, vector_size, min_n, max_n, bucket, compatible_hash): self.min_n = min_n self.max_n = max_n self.bucket = bucket # count of buckets, fka num_ngram_vectors - self.compatible_hash = compatible_hash + self.compatible_hash = True @classmethod def load(cls, fname_or_handle, **kwargs): model = super(FastTextKeyedVectors, cls).load(fname_or_handle, **kwargs) if isinstance(model, FastTextKeyedVectors): - if not hasattr(model, 'compatible_hash'): - model.compatible_hash = False - _try_upgrade(model) + if not hasattr(model, 'compatible_hash') or model.compatible_hash is False: + raise TypeError("Pre-gensim-3.8.x Fasttext models with nonstandard hashing are no longer compatible." + "Loading into gensim-3.8.3 & re-saving may create a compatible model.") return model def __contains__(self, word): @@ -1327,7 +1306,7 @@ def get_vector(self, word, use_norm=False): else: word_vec = np.zeros(self.vectors_ngrams.shape[1], dtype=np.float32) ngram_weights = self.vectors_ngrams - ngram_hashes = ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash) + ngram_hashes = ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket) if len(ngram_hashes) == 0: # # If it is impossible to extract _any_ ngrams from the input @@ -1469,7 +1448,7 @@ def recalc_word_ngram_buckets(self): for i, word in enumerate(self.index_to_key): self.buckets_word[i] = np.array( - ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket, self.compatible_hash), + ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket), dtype=np.uint32, ) @@ -1482,34 +1461,6 @@ def _pad_random(m, new_rows, rand): return vstack([m, suffix]) -def _rollback_optimization(kv): - """Undo the optimization that pruned buckets. - - This unfortunate optimization saves memory and CPU cycles, but breaks - compatibility with Facebook's model by introducing divergent behavior - for OOV words. - - """ - logger.warning( - "This saved FastText model was trained with an optimization we no longer support. " - "The current Gensim version automatically reverses this optimization during loading. " - "Save the loaded model to a new file and reload to suppress this message." - ) - assert hasattr(kv, 'hash2index') - assert hasattr(kv, 'bucket') - - kv.vectors_ngrams = _unpack(kv.vectors_ngrams, kv.bucket, kv.hash2index) - if hasattr(kv, 'vectors_ngrams_lockf'): - # just clobber with no-op lockf array: vanishingly unlikely this experimental feature used in old files - kv.vectors_ngrams_lockf = ones(1, dtype=REAL) - - # - # We have replaced num_ngram_vectors with a property and deprecated it. - # We can't delete it because the new attribute masks the member. - # - del kv.hash2index - - def _unpack(m, num_rows, hash2index, seed=1, fill=None): """Restore the array to its natural shape, undoing the optimization. @@ -1587,19 +1538,6 @@ def _unpack(m, num_rows, hash2index, seed=1, fill=None): return m -def _try_upgrade(wv): - if hasattr(wv, 'hash2index'): - _rollback_optimization(wv) - - if not hasattr(wv, 'compatible_hash'): - logger.warning( - "This older model was trained with a buggy hash function. " - "The model will continue to work, but consider training it " - "from scratch." - ) - wv.compatible_hash = False - - # # UTF-8 bytes that begin with 10 are subsequent bytes of a multi-byte sequence, # as opposed to a new character. @@ -1623,7 +1561,7 @@ def _is_utf8_continue(b): return _byte_to_int(b) & _MB_MASK == _MB_START -def ft_ngram_hashes(word, minn, maxn, num_buckets, fb_compatible=True): +def ft_ngram_hashes(word, minn, maxn, num_buckets): """Calculate the ngrams of the word and hash them. Parameters @@ -1636,21 +1574,14 @@ def ft_ngram_hashes(word, minn, maxn, num_buckets, fb_compatible=True): Maximum ngram length num_buckets : int The number of buckets - fb_compatible : boolean, optional - True for compatibility with the Facebook implementation. - False for compatibility with the old Gensim implementation. Returns ------- A list of hashes (integers), one per each detected ngram. """ - if fb_compatible: - encoded_ngrams = compute_ngrams_bytes(word, minn, maxn) - hashes = [ft_hash_bytes(n) % num_buckets for n in encoded_ngrams] - else: - text_ngrams = compute_ngrams(word, minn, maxn) - hashes = [ft_hash_broken(n) % num_buckets for n in text_ngrams] + encoded_ngrams = compute_ngrams_bytes(word, minn, maxn) + hashes = [ft_hash_bytes(n) % num_buckets for n in encoded_ngrams] return hashes diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index 140e01da9e..0bdebe6606 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -697,30 +697,6 @@ cpdef ft_hash_bytes(bytes bytez): return h -cpdef ft_hash_broken(unicode string): - """Calculate hash based on `string`. - - This implementation is broken, see https://github.com/RaRe-Technologies/gensim/issues/2059. - It is here only for maintaining backwards compatibility with older models. - - Parameters - ---------- - string : unicode - The string whose hash needs to be calculated. - - Returns - ------- - unsigned int - The hash of the string. - - """ - cdef unsigned int h = 2166136261 - for c in string: - h ^= ord(c) - h *= 16777619 - return h - - cpdef compute_ngrams(word, unsigned int min_n, unsigned int max_n): """Get the list of all possible ngrams for a given word. diff --git a/gensim/test/test_data/compatible-hash-false.model b/gensim/test/test_data/compatible-hash-false.model deleted file mode 100644 index 5a76fa7f6b6ea2d4eb207717edf9ec1608b1f923..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 14826 zcmeHuc~lf_mmeUYpaP=c0*WXqA}Wg;&{ZHVfF-CcDk|7EO+%w>)!l%gD9A25qA05f zh#;VBA`K16z1b$oB$@1A_RM6NWU@~7%p{XvHR$_&$vMei`OcYhUOCcD)vf#7d!M`C zr^+34S^7LvcJ8s<{0x1L=~$-LWH##`nO!g9OS1AswmV!Q+n31>fhmr5c6K@W+6;N! z_ooWY**PXxoi-;&lb@&2M4wTl*O~K;rXO@l%Eg;& z7iAaF>{=#o3`|)M9BZ)hCS6`(Zb8X0UA|F&EVnSnoUJt)wI!|^qh6PvXEGZLb>MgN zDwJzp#_ChqK@uChmpJ#eSXTT-0ynWHsl=Y`6iHS$)qyscLD zQp?_flD*_DIn~J9*HC7dOA7Gh4jX5gC*+-yon&7t?^4S?fzmn~K9%g7l$3PokMPg- zOR_V|yVL6p9w~(o$GWo)WaZv#vg%Mc_tFx5plNQVrI2GZM%G>2aE+{~+{;ZW9ktt$ zHH{mO3S5^!ePrGFteC9yl^0!beOJ1QhPZKyaF2UaBSTcY7{#qzahn$Rsp3_8xm%z` zdY4Pj$r2P9EpRzc-cqskXfW0sqlVi;B$a!L+qXN4Pj8R~G<|!@-)QMPy|Gt?aRa>) zHqp03WZlHg*M+!vu8-1W*$LF?lB){cy+tSqq85QBL}2csZfYXyIclVrD*ntb0%+;@ zb}w2C3*#Y`c^fsUc%ozjEd>kI1qMFybds=%7a0umQV_jY9gN{leDC+flhmgPR_!>r zU6n*{(^Xu>qx2$*nzCq2#pU}S2u~iWY3W=X_0W7RU3rJOI#nRcg{*=zf!{MbxEdc7+`(VK#0o9! z6cna*3(p>f3C`5QLs4h0Qmw#M;q)2IHb;UeD}ABb3|jOxVcV(d*|jK|*>H}m*}UW^ z(%jQhf#!E#$E|wmiV;q5T`9iL>S>vl?(j>V6k@WtGm_qOKfNQX=Keuibc;8uw(?jo zedEQ`S5!0+?5C3V7@*dEYI(0&_D@f7QYh(SRUTqYk4Gz3bU42LC0L(nD7Z~W#)z$lifm59o=6QH&Ls^0yp=h|ZVnequ6%*itOlmfFIF!q2M-;}!nFzXad z2EBaR26U!Oj!=Mt0b~cD-wttgUIWtRWyp~>pr|rgr2uUKkOP2d_!ll~t7V&H!3HCi z$u=VVs@S!V}m(cCdVo;9)MW~nBUqRHo!||vZMg62jE5k z4upSn|4zDDzHEa@ER&NI7-zt20?hAU`9qI2nDi3)iVfy!nS4!wQ3UP@m^FbbyZ&99 z%qCfFgS}oRCx6Gc`6qme;I&4*e8UEGvrNAA9mwq`AVps3dO5`gbGuBw^L^2+KP{@r zqF9l|T^m$tnVj|=llxDYRwai0xo3mYl*#wMLwWoJrL3pTleIRO^fFoZeZ6fzt*1o4 zA56*_Hc)+;oT-2+YU&EmH8tG;&;nzAf!>H6$g)8i%H-_tNWFd_EwqKu)ljD~WtZyZ z2a=P`h-@YmrUX~{VVay1XgF;+VTd%u7>*bO!$re6LxAC!A;XYhxNJ}xZW!(w?ioT2 zR}3iztszUFvKa)d1yIeZ+2Sef`dbmD#8J?q24_aP-;t(x!3oq~zeA48tCuDWkAzmDJeX8Qu7o2f%tcbb0U)XzK99jcRUy$F*MLL7o%1lBQxrn&feML)P$U z>J#{jdY6be_}O2~eS8=XyyP|&Xc+KaPs>lZW0weQjRCmPc*y32-*au0c$XH_RrYwS zSMV$*cx0-$N$-T?1uo(RT0T$9(h+VHsFs)GL|1C#CZX&K&5#u_hbIIOS-lS!zwSfO zT*{`ef#PoJr;k80L>)9i)?BLE4uAz$VDpE3Is_>JL|e~t9io7>fZt`(;x(E%Ma%oC ziodCh^bX=fOj`65vI}TP;M!aJ!A2i7tS9TkmjE%XY%4AMUBIuSyuyn@JS}4j0zF-@K#k9Fk={KlA**jOxH`3; 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Not crashing is a success.""" # @@ -1128,20 +1111,10 @@ class HashCompatibilityTest(unittest.TestCase): def test_compatibility_true(self): m = FT_gensim.load(datapath('compatible-hash-true.model')) self.assertTrue(m.wv.compatible_hash) - self.assertEqual(m.bucket, m.wv.bucket) - - def test_compatibility_false(self): - # - # Originally obtained using and older version of gensim (e.g. 3.6.0). - # - m = FT_gensim.load(datapath('compatible-hash-false.model')) - self.assertFalse(m.wv.compatible_hash) - self.assertEqual(m.bucket, m.wv.bucket) def test_hash_native(self): m = load_native() self.assertTrue(m.wv.compatible_hash) - self.assertEqual(m.bucket, m.wv.bucket) class FTHashResultsTest(unittest.TestCase): @@ -1568,7 +1541,7 @@ def _check_roundtrip(self, sg): self.assertEqual(model_trained.negative, model_loaded.negative) self.assertEqual(model_trained.hs, model_loaded.hs) self.assertEqual(model_trained.sg, model_loaded.sg) - self.assertEqual(model_trained.bucket, model_loaded.bucket) + self.assertEqual(model_trained.wv.bucket, model_loaded.wv.bucket) self.assertEqual(model_trained.wv.min_n, model_loaded.wv.min_n) self.assertEqual(model_trained.wv.max_n, model_loaded.wv.max_n) self.assertEqual(model_trained.sample, model_loaded.sample) @@ -1729,21 +1702,6 @@ def test_cbow(self): self._check_load_fasttext_format(sg=0) -class TestFastTextKeyedVectors(unittest.TestCase): - def test_ft_kv_backward_compat_w_360(self): - kv = KeyedVectors.load(datapath("ft_kv_3.6.0.model.gz")) - ft_kv = FastTextKeyedVectors.load(datapath("ft_kv_3.6.0.model.gz")) - - expected = ['trees', 'survey', 'system', 'graph', 'interface'] - actual = [word for (word, similarity) in kv.most_similar("human", topn=5)] - - self.assertEqual(actual, expected) - - actual = [word for (word, similarity) in ft_kv.most_similar("human", topn=5)] - - self.assertEqual(actual, expected) - - class UnpackTest(unittest.TestCase): def test_sanity(self): m = np.array(range(9)) From 278c2bd5ba8732a49c1c59deb93f3c7fc60c4790 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 11:15:56 -0700 Subject: [PATCH 40/60] use numpy-recommended PRNG constructor --- gensim/models/fasttext.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 41e15396ea..b75f195ab6 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -1343,8 +1343,7 @@ def init_ngrams_weights(self, seed): """ self.recalc_word_ngram_buckets() - rand_obj = np.random - rand_obj.seed(seed) + rand_obj = np.random.default_rng(seed=seed) # use new instance of numpy's recommended generator/algorithm lo, hi = -1.0 / self.vector_size, 1.0 / self.vector_size vocab_shape = (len(self), self.vector_size) From 5c7eb1ca2d067cc41fce3a6dee9b275ffc0e53b8 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 11:46:41 -0700 Subject: [PATCH 41/60] add sg to FastTextConfig & consult it; rm remaining broken-hash cruft --- gensim/models/fasttext_inner.pxd | 4 ++-- gensim/models/fasttext_inner.pyx | 9 ++++----- gensim/test/test_fasttext.py | 25 ++----------------------- 3 files changed, 8 insertions(+), 30 deletions(-) diff --git a/gensim/models/fasttext_inner.pxd b/gensim/models/fasttext_inner.pxd index fe66e3c545..31a1b1d35f 100644 --- a/gensim/models/fasttext_inner.pxd +++ b/gensim/models/fasttext_inner.pxd @@ -46,7 +46,7 @@ cdef struct FastTextConfig: # # Model parameters. These get copied as-is from the Python model. # - int hs, negative, sample, size, window, cbow_mean, workers + int sg, hs, negative, sample, size, window, cbow_mean, workers REAL_t alpha # @@ -146,4 +146,4 @@ cdef void fasttext_fast_sentence_cbow_neg(FastTextConfig *c, int i, int j, int k cdef void fasttext_fast_sentence_cbow_hs(FastTextConfig *c, int i, int j, int k) nogil -cdef void fasttext_train_any(FastTextConfig *c, int num_sentences, int sg) nogil +cdef void fasttext_train_any(FastTextConfig *c, int num_sentences) nogil diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index 0bdebe6606..36ccdb91b9 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -454,6 +454,7 @@ cdef void init_ft_config(FastTextConfig *c, model, alpha, _work, _neu1): Private working memory for each worker. """ + c.sg = model.sg c.hs = model.hs c.negative = model.negative c.sample = (model.sample != 0) @@ -568,7 +569,7 @@ cdef object populate_ft_config(FastTextConfig *c, wv, buckets_word, sentences): return effective_words, effective_sentences -cdef void fasttext_train_any(FastTextConfig *c, int num_sentences, int sg) nogil: +cdef void fasttext_train_any(FastTextConfig *c, int num_sentences) nogil: """Performs training on a fully initialized and populated configuration. Parameters @@ -577,8 +578,6 @@ cdef void fasttext_train_any(FastTextConfig *c, int num_sentences, int sg) nogil A pointer to the configuration struct. num_sentences : int The number of sentences to train. - sg : int - 1 for skipgram, 0 for CBOW. """ cdef: @@ -611,7 +610,7 @@ cdef void fasttext_train_any(FastTextConfig *c, int num_sentences, int sg) nogil # window_start = max(sentence_start, i - c.window + c.reduced_windows[i]) # window_end = min(sentence_end, i + c.window + 1 - c.reduced_windows[i]) # - if sg == 0: + if c.sg == 0: if c.hs: fasttext_fast_sentence_cbow_hs(c, i, window_start, window_end) if c.negative: @@ -667,7 +666,7 @@ def train_batch_any(model, sentences, alpha, _work, _neu1): # release GIL & train on all sentences in the batch with nogil: - fasttext_train_any(&c, num_sentences, 0) + fasttext_train_any(&c, num_sentences) return num_words diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index a997ca9f8c..2a492dacc3 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -20,7 +20,7 @@ from gensim.models.keyedvectors import KeyedVectors from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences import gensim.models._fasttext_bin -from gensim.models.fasttext_inner import compute_ngrams, compute_ngrams_bytes, ft_hash_broken, ft_hash_bytes +from gensim.models.fasttext_inner import compute_ngrams, compute_ngrams_bytes, ft_hash_bytes import gensim.models.fasttext @@ -1164,7 +1164,6 @@ def hash_main(alg): assert six.PY3, 'this only works under Py3' hashmap = { - 'cy_broken': ft_hash_broken, 'cy_bytes': ft_hash_bytes, } try: @@ -1188,7 +1187,7 @@ def setUp(self): # # $ echo word1 ... wordN | python -c 'from gensim.test.test_fasttext import hash_main;hash_main("alg")' # noqa: E501 # - # where alg is one of py_bytes, py_broken, cy_bytes, cy_broken. + # where alg is cy_bytes (previous options had included: py_bytes, py_broken, cy_bytes, cy_broken.) # self.expected = { @@ -1207,31 +1206,11 @@ def setUp(self): u'札幌': 3909947444, u'西区': 3653372632, } - self.expected_broken = { - u'команда': 962806708, - u'маленьких': 3633597485, - u'друзей': 214728041, - u'возит': 3590926132, - u'грузы': 3674544745, - u'всех': 3931012458, - u'быстрей': 822471432, - u'mysterious': 1903186891, - u'asteroid': 1988297200, - u'odyssey': 310195777, - u'introduction': 2848265721, - u'北海道': 4017049120, - u'札幌': 1706980764, - u'西区': 1113327900, - } def test_cython(self): actual = {k: ft_hash_bytes(k.encode('utf-8')) for k in self.expected} self.assertEqual(self.expected, actual) - def test_cython_broken(self): - actual = {k: ft_hash_broken(k) for k in self.expected} - self.assertEqual(self.expected_broken, actual) - # # Run with: From 23805d14dd7388090b9be1b1c12edbd4165ffd0c Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 13:19:57 -0700 Subject: [PATCH 42/60] reorg conditional packages for clarity --- setup.py | 46 ++++++++++++++++++++++------------------------ 1 file changed, 22 insertions(+), 24 deletions(-) diff --git a/setup.py b/setup.py index a07018fd39..3cc3055a2f 100644 --- a/setup.py +++ b/setup.py @@ -170,7 +170,7 @@ def run(self): Features --------- -* All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core), +* All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM, streamed, out-of-core) * **Intuitive interfaces** * easy to plug in your own input corpus/datastream (simple streaming API) @@ -260,7 +260,10 @@ def run(self): distributed_env = ['Pyro4 >= 4.27'] -linux_testenv = [ +visdom_req = ['visdom >= 0.1.8, != 0.1.8.7'] + +# packages included for build-testing everywhere +core_testenv = [ 'pytest', 'pytest-rerunfailures', 'mock', @@ -270,16 +273,22 @@ def run(self): 'Morfessor==2.0.2a4', 'python-Levenshtein >= 0.10.2', 'scikit-learn', - # The following packages are commented out because they don't install on Windows. So skip the - # related tests in AppVeyor. We still test them in Linux via Travis, see linux_testenv below. - # See https://github.com/RaRe-Technologies/gensim/pull/2814 - # 'tensorflow', - # 'keras', - 'pyemd', # see below; keep as last until appveyor issue resolved ] -# temporarily remove pyemd to work around appveyor issues -win_testenv = linux_testenv[:-1] +# Add additional requirements for testing on Linux that are skipped on Windows. +linux_testenv = core_testenv[:] + visdom_req + ['pyemd', ] +if sys.version_info >= (3, 7): + # HACK: Installing tensorflow causes a segfault in Travis on py3.6. Other Pythons work – a mystery. + # See https://github.com/RaRe-Technologies/gensim/pull/2814#issuecomment-621477948 + linux_testenv += [ + 'tensorflow', + 'keras==2.3.1', + ] + +# Skip problematic/uninstallable packages (& thus related conditional tests) in Windows builds. +# We still test them in Linux via Travis, see linux_testenv above. +# See https://github.com/RaRe-Technologies/gensim/pull/2814 +win_testenv = core_testenv[:] # # This list partially duplicates requirements_docs.txt. @@ -291,8 +300,8 @@ def run(self): # # https://packaging.python.org/discussions/install-requires-vs-requirements/ # -visdom_req = ['visdom >= 0.1.8, != 0.1.8.7'] -docs_testenv = win_testenv + distributed_env + visdom_req + [ + +docs_testenv = core_testenv + distributed_env + visdom_req + [ 'sphinx <= 2.4.4', # avoid `sphinx >= 3.0` that breaks the build 'sphinx-gallery', 'sphinxcontrib.programoutput', @@ -316,17 +325,6 @@ def run(self): 'pandas', ] -# Add additional requirements for testing on Linux. We skip some tests on Windows, -# because the libraries below are too tricky to install there. -linux_testenv = win_testenv[:] + visdom_req -if sys.version_info >= (3, 7): - # HACK: Installing tensorflow causes a segfault in Travis on py3.6. Other Pythons work – a mystery. - # See https://github.com/RaRe-Technologies/gensim/pull/2814#issuecomment-621477948 - linux_testenv += [ - 'tensorflow', - 'keras==2.3.1', - ] - NUMPY_STR = 'numpy >= 1.11.3' # # We pin the Cython version for reproducibility. We expect our extensions @@ -340,7 +338,7 @@ def run(self): 'scipy >= 0.18.1', 'six >= 1.5.0', 'smart_open >= 1.8.1', - "dataclasses; python_version < '3.7'", + "dataclasses; python_version < '3.7'", # pre-py3.7 needs `dataclasses` backport for use of `dataclass` in doc2vec.py ] setup_requires = [NUMPY_STR] From f5b902c2526eff79bb36e64baea371027dc40680 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 22:25:02 -0700 Subject: [PATCH 43/60] comments, names, refactoring, randomization --- gensim/models/keyedvectors.py | 234 ++++++++++++++++------------------ gensim/utils.py | 3 + 2 files changed, 113 insertions(+), 124 deletions(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index f48ec91de0..c68970c4ed 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -64,22 +64,20 @@ .. sourcecode:: pycon - >>> from gensim.test.utils import common_texts + >>> from gensim.test.utils import lee_corpus_list >>> from gensim.models import Word2Vec >>> - >>> model = Word2Vec(common_texts, size=100, window=5, min_count=1, workers=4) + >>> model = Word2Vec(common_texts, size=24, epochs=100) >>> word_vectors = model.wv Persist the word vectors to disk with .. sourcecode:: pycon - >>> 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') + >>> word_vectors.save('vectors.kv') + >>> reloaded_word_vectors = KeyedVectors.load('vectors.kv') The vectors can also be instantiated from an existing file on disk in the original Google's word2vec C format as a KeyedVectors instance @@ -166,15 +164,10 @@ import logging import sys import itertools +import warnings from itertools import chain -from collections import UserList, UserDict from numbers import Integral -try: - from queue import Queue, Empty -except ImportError: - from Queue import Queue, Empty # noqa:F401 - from numpy import dot, float32 as REAL, \ double, array, zeros, vstack, \ ndarray, sum as np_sum, prod, argmax, dtype, ascontiguousarray, \ @@ -184,32 +177,38 @@ from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc from gensim.corpora.dictionary import Dictionary from gensim.utils import deprecated -from six import string_types, integer_types -from six.moves import zip, range from scipy import stats logger = logging.getLogger(__name__) -KEY_TYPES = (string_types, integer_types, np.integer) +KEY_TYPES = (str, int, np.integer) class KeyedVectors(utils.SaveLoad): - def __init__(self, vector_size, mapfile_path=None): + def __init__(self, vector_size, count=0, dtype=REAL, mapfile_path=None): """Mapping between keys (such as words) and vectors for :class:`~gensim.models.Word2Vec` and related models. Used to perform operations on the vectors such as vector lookup, distance, similarity etc. + To support the needs of specific models and other downstream uses, each key may also have + additional attributes set and read via the `set_vecattr(key, attr, value)` and `get_vecattr(key, attr)` + methods. Note that all such attributes under the same `attr` name must have compatible `numpy` + types, as the type and storage array for such attributes is established by the 1st time such + `attr` is set. + """ self.vector_size = vector_size - self.vectors = zeros((0, vector_size), dtype=REAL) # fka (formerly known as) syn0 - self.norms = None - - self.index_to_key = [] # fka index2entity or index2word + # pre-allocating `index_to_key` to full size helps avoid redundant re-allocations, esp for `expandos` + self.index_to_key = [None] * count # fka index2entity or index2word + self.next_index = 0 # pointer to where next new entry will land self.key_to_index = {} + self.vectors = zeros((count, vector_size), dtype=dtype) # fka (formerly known as) syn0 + self.norms = None + self.expandos = {} # dynamically-expandable per-vector named, numpy-typed attributes self.mapfile_path = mapfile_path @@ -237,7 +236,7 @@ def _load_specials(self, *args, **kwargs): self._upconvert_old_vocab() def _upconvert_old_vocab(self): - """Convert a loaded, prior-version instance that had a 'vocab' dict of data objects""" + """Convert a loaded, pre-gensim-4.0.0 version instance that had a 'vocab' dict of data objects""" old_vocab = self.__dict__.pop('vocab', None) self.key_to_index = {} for k in old_vocab.keys(): @@ -256,7 +255,7 @@ def allocate_vecattrs(self, attrs=None, types=None): even if other properties (vectors array) hasn't yet been allocated or expanded. So this allocation targets that size. """ - # with no arguments, simply adjust sizes of existing + # with no arguments, adjust lengths of existing storage arrays to match length of index_to_key if attrs is None: attrs = list(self.expandos.keys()) types = [self.expandos[attr].dtype for attr in attrs] @@ -276,13 +275,13 @@ def allocate_vecattrs(self, attrs=None, types=None): prev_expando[0:min(prev_count, target_size), ] def set_vecattr(self, key, attr, val): - """ TODO """ + """Set attribute associated with given key to value. TODO: param docs""" self.allocate_vecattrs(attrs=[attr], types=[type(val)]) index = self.get_index(key) self.expandos[attr][index] = val def get_vecattr(self, key, attr): - """ TODO """ + """Get attribute value associate with given key. TODO: param docs""" index = self.get_index(key) return self.expandos[attr][index] @@ -343,7 +342,7 @@ def get_index(self, key): """ if key in self.key_to_index: return self.key_to_index[key] - elif isinstance(key, (integer_types, np.integer)) and key < len(self.index_to_key): + elif isinstance(key, (int, np.integer)) and key < len(self.index_to_key): return key else: raise KeyError("Key '%s' not present" % key) @@ -383,6 +382,34 @@ def word_vec(self, *args, **kwargs): """Compatibility alias for get_vector()""" return self.get_vector(*args, **kwargs) + def add_one(self, key, vector): + """Add one new vector at the given key, into existing slot if available. + + Warning: using this repeatedly is inefficient, requiring a full reallocation & copy, + if this instance hasn't been preallocated to be ready fro such incremental additions. + + returns: actual index used TODO: other param docs + """ + + target_index = self.next_index + if target_index >= len(self) or self.index_to_key[target_index] is not None: + # must append at end by expanding existing structures + target_index = len(self) + warnings.warn( + "Adding single vectors to a KeyedVectors which grows by one each time can be costly. " + "Consider adding in batches or preallocating to the required size.", + UserWarning) + self.add([key], [vector]) + self.allocate_vecattrs() # grow any adjunct arrays + self.next_index = target_index + 1 + else: + # can add to existing slot + self.index_to_key[target_index] = key + self.key_to_index[key] = target_index + self.vectors[target_index] = vector + self.next_index += 1 + return target_index + def add(self, keys, weights, extras=None, replace=False): """Append keys and their vectors in a manual way. If some key is already in the vocabulary, the old vector is kept unless `replace` flag is True. @@ -489,7 +516,7 @@ def rank(self, key1, key2): """Rank of the distance of `key2` from `key1`, in relation to distances of all keys from `key1`.""" return len(self.closer_than(key1, key2)) + 1 - # backward compatibility + # backward compatibility; some would be annotated `@deprecated` if that stacked with @property/.setter @property def vectors_norm(self): self.fill_norms() @@ -539,35 +566,6 @@ def vocab(self): def vocab(self, value): self.vocab() # trigger above NotImplementedError - @property - def pseudovocab(self): - """ pseudodict providing pseudovocab objects - - not efficient, temp backcompat workaround 'just in case' a .vocab use can't adapt - """ - class Vocaboid(object): - def __init__(self, kv, index): - self.kv = kv - self.index = index - - def __getattr__(self, attr): - if attr not in self.kv.expandos: - raise AttributeError("Attribute '{0}' not in parent KeyedVectors".format(attr)) - return self.kv.get_vecattr(self.index, attr) - - class VocaboidDict(UserDict): - def __init__(self, kv): - super(VocaboidDict, self).__init__() - self.data = kv - - def __getitem__(self, key): - return Vocaboid(self.data, self.data.get_index(key)) - - def __contains__(self, key): - return key in self.data - - return VocaboidDict(self) - def sort_by_descending_frequency(self): """Sort the vocabulary so the most frequent words have the lowest indexes.""" if not len(self): @@ -893,7 +891,7 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): self.fill_norms() - if isinstance(positive, string_types) and not negative: + if isinstance(positive, str) and not negative: # allow calls like most_similar_cosmul('dog'), as a shorthand for most_similar_cosmul(['dog']) positive = [positive] @@ -903,11 +901,11 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): } positive = [ - self.word_vec(word, use_norm=True) if isinstance(word, string_types) else word + self.word_vec(word, use_norm=True) if isinstance(word, str) else word for word in positive ] negative = [ - self.word_vec(word, use_norm=True) if isinstance(word, string_types) else word + self.word_vec(word, use_norm=True) if isinstance(word, str) else word for word in negative ] @@ -927,18 +925,20 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): result = [(self.index_to_key[sim], float(dists[sim])) for sim in best if sim not in all_words] return result[:topn] - def doesnt_match(self, words): - """Which key from the given list doesn't go with the others? + def rank_by_centrality(self, words, use_norm=True): + """Rank the given words by similarity to the centroid of all the words. Parameters ---------- words : list of str List of keys. + use_norm : bool, optional + Whether to calculate centroid using unit-normed vectors; default True. Returns ------- - str - The key further away from the mean of all keys. + list of (float, str) + Ranked list of (similarity, key), most-similar to the centroid first. """ self.fill_norms() @@ -949,10 +949,26 @@ def doesnt_match(self, 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) + vectors = vstack([self.word_vec(word, use_norm=use_norm) 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] + return sorted(zip(dists, used_words), reverse=True) + + def doesnt_match(self, words): + """Which key from the given list doesn't go with the others? + + Parameters + ---------- + words : list of str + List of keys. + + Returns + ------- + str + The key further away from the mean of all keys. + + """ + return self.rank_by_centrality(words)[-1][1] @staticmethod def cosine_similarities(vector_1, vectors_all): @@ -1592,6 +1608,8 @@ def get_keras_embedding(self, train_embeddings=False): return layer def _upconvert_old_d2vkv(self): + """Convert a deserialized older Doc2VecKeyedVectors instance to latest generic KeyedVectors""" + self.vocab = self.doctags self._upconvert_old_vocab() # destroys 'vocab', fills 'key_to_index' & 'extras' for k in self.key_to_index.keys(): @@ -1644,14 +1662,16 @@ def __str__(self): # Functions for internal use by _load_word2vec_format function -def _add_word_to_result(result, counts, word, weights, vocab_size): +def _add_word_to_kv(kv, counts, word, weights, vocab_size): - word_id = len(result) - if result.has_index_for(word): + if kv.has_index_for(word): logger.warning("duplicate word '%s' in word2vec file, ignoring all but first", word) return + word_id = kv.add_one(word, weights) + if counts is None: # most common scenario: no vocab file given. just make up some bogus counts, in descending order + # TODO: make this faking optional, include more realistic (Zipf-based) fake numbers word_count = vocab_size - word_id elif word in counts: # use count from the vocab file @@ -1659,18 +1679,15 @@ def _add_word_to_result(result, counts, word, weights, vocab_size): else: logger.warning("vocabulary file is incomplete: '%s' is missing", word) word_count = None + kv.set_vecattr(word, 'count', word_count) - result.key_to_index[word] = word_id - result.index_to_key.append(word) - result.set_vecattr(word, 'count', word_count) - result.vectors[word_id] = weights - -def _add_bytes_to_result(result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors): +def _add_bytes_to_kv(kv, counts, chunk, vocab_size, vector_size, datatype, unicode_errors): start = 0 processed_words = 0 bytes_per_vector = vector_size * dtype(REAL).itemsize - max_words = vocab_size - len(result) + max_words = vocab_size - kv.next_index # don't read more than kv preallocated to hold + assert max_words > 0 for _ in range(max_words): i_space = chunk.find(b' ', start) i_vector = i_space + 1 @@ -1682,22 +1699,22 @@ def _add_bytes_to_result(result, counts, chunk, vocab_size, vector_size, datatyp # Some binary files are reported to have obsolete new line in the beginning of word, remove it word = word.lstrip('\n') vector = frombuffer(chunk, offset=i_vector, count=vector_size, dtype=REAL).astype(datatype) - _add_word_to_result(result, counts, word, vector, vocab_size) + _add_word_to_kv(kv, counts, word, vector, vocab_size) start = i_vector + bytes_per_vector processed_words += 1 return processed_words, chunk[start:] -def _word2vec_read_binary(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size): +def _word2vec_read_binary(fin, kv, counts, vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size): chunk = b'' tot_processed_words = 0 while tot_processed_words < vocab_size: new_chunk = fin.read(binary_chunk_size) chunk += new_chunk - processed_words, chunk = _add_bytes_to_result( - result, counts, chunk, vocab_size, vector_size, datatype, unicode_errors) + processed_words, chunk = _add_bytes_to_kv( + kv, counts, chunk, vocab_size, vector_size, datatype, unicode_errors) tot_processed_words += processed_words if len(new_chunk) < binary_chunk_size: break @@ -1705,13 +1722,13 @@ def _word2vec_read_binary(fin, result, counts, vocab_size, vector_size, datatype raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") -def _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, encoding): +def _word2vec_read_text(fin, kv, counts, vocab_size, vector_size, datatype, unicode_errors, encoding): for line_no in range(vocab_size): line = fin.readline() if line == b'': raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?") word, weights = _word2vec_line_to_vector(line, datatype, unicode_errors, encoding) - _add_word_to_result(result, counts, word, weights, vocab_size) + _add_word_to_kv(kv, counts, word, weights, vocab_size) def _word2vec_line_to_vector(line, datatype, unicode_errors, encoding): @@ -1797,25 +1814,23 @@ def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8' vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format if limit: vocab_size = min(vocab_size, limit) - result = cls(vector_size) - result.vector_size = vector_size - result.vectors = zeros((vocab_size, vector_size), dtype=datatype) + kv = cls(vector_size, vocab_size, dtype=datatype) if binary: - _word2vec_read_binary(fin, result, counts, + _word2vec_read_binary(fin, kv, counts, vocab_size, vector_size, datatype, unicode_errors, binary_chunk_size) else: - _word2vec_read_text(fin, result, counts, vocab_size, vector_size, datatype, unicode_errors, encoding) - if result.vectors.shape[0] != len(result): + _word2vec_read_text(fin, kv, counts, vocab_size, vector_size, datatype, unicode_errors, encoding) + if kv.vectors.shape[0] != len(kv): logger.info( "duplicate words detected, shrinking matrix size from %i to %i", - result.vectors.shape[0], len(result) + kv.vectors.shape[0], len(kv) ) - result.vectors = ascontiguousarray(result.vectors[: len(result)]) - assert (len(result), vector_size) == result.vectors.shape + kv.vectors = ascontiguousarray(kv.vectors[: len(kv)]) + assert (len(kv), vector_size) == kv.vectors.shape - logger.info("loaded %s matrix from %s", result.vectors.shape, fname) - return result + logger.info("loaded %s matrix from %s", kv.vectors.shape, fname) + return kv def load_word2vec_format(*args, **kwargs): @@ -1824,42 +1839,13 @@ def load_word2vec_format(*args, **kwargs): def pseudorandom_weak_vector(size, seed_string=None, hashfxn=hash): - """Get a 'random' vector (but somewhat deterministic, at least - within the same Python 3 launch or PYTHONHASHSEED, if seed_string - supplied). + """Get a 'random' vector (but deterministically derived from seed_string if supplied). Useful for initializing KeyedVectors that will be the starting projection/input layers of _2Vec models. """ if seed_string: - once = np.random.RandomState(hashfxn(seed_string) & 0xffffffff) + once = np.random.Generator(np.random.SFC64(hashfxn(seed_string) & 0xffffffff)) else: - once = np.random - return (once.rand(size).astype(REAL) - 0.5) / size - - -class ConcatList(UserList): - """Pseudo-list that stitches together multiple underlying sequences, but - only offers indexed-access and iteration. - - (Used to support KeyedVectors optimization in case of mixed plain-int and - string keys, where all plain-int keys are represented by a simple `range()` - object, followed by a real list.) - - # TODO: implement or stub as NotImplemented other necessary methods, - # especially slicing? - """ - def __getitem__(self, index): - for subseq in self.data: - if index >= len(subseq): - index -= len(subseq) - continue - return subseq[index] - else: - raise IndexError("ConcatList index out of range") - - def __iter__(self): - return iter(chain(*self.data)) - - def __len__(self): - return sum(len(subseq) for subseq in self.data) + once = utils.default_prng + return (once.random(size).astype(REAL) - 0.5) / size diff --git a/gensim/utils.py b/gensim/utils.py index 90c9279338..bb9ee2fa02 100644 --- a/gensim/utils.py +++ b/gensim/utils.py @@ -64,6 +64,9 @@ ) """An exception that gensim code raises when Cython extensions are unavailable.""" +#: A default, shared numpy-Generator-based PRNG for any/all uses that don't require seeding +default_prng = np.random.default_rng() + def get_random_state(seed): """Generate :class:`numpy.random.RandomState` based on input seed. From 7b571b2fe9c6d29e6852814aaaa2162bca970200 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 22:38:12 -0700 Subject: [PATCH 44/60] Apply suggestions from code review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Radim Řehůřek --- gensim/models/keyedvectors.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index c68970c4ed..41310128c5 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -303,7 +303,7 @@ def resize_vectors(self): def randomly_initialize_vectors(self, indexes=None, seed=0): """Initialize vectors with low-magnitude random vectors, as is typical for pre-trained - Word2Vec and related models. + Word2Vec and related models. """ if indexes is None: @@ -378,7 +378,7 @@ def get_vector(self, key, use_norm=False): result.setflags(write=False) # disallow direct tampering that would invalidate `norms` etc return result - def word_vec(self, *args, **kwargs): + word_vec = get_vector # Compatibility alias for get_vector() """Compatibility alias for get_vector()""" return self.get_vector(*args, **kwargs) @@ -421,7 +421,7 @@ def add(self, keys, weights, extras=None, replace=False): weights: list of numpy.ndarray or 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 keys which already exist in the map + Flag indicating whether to replace vectors for keys which already exist in the map; if True - replace vectors, otherwise - keep old vectors. """ @@ -527,7 +527,7 @@ def fill_norms(self, force=False): Ensure per-vector norms are available. (Any code which modifies vectors should ensure the - accompanying norms are recalculated, or 'None'-out + accompanying norms are either recalculated or 'None', to trigger a full recalc later. 'norms' to trigger full recalc later.) """ if self.norms is None or force: @@ -574,9 +574,9 @@ def sort_by_descending_frequency(self): self.index_to_key = list(np.array(self.index_to_key)[count_sorted_indexes]) self.allocate_vecattrs() for k in self.expandos: - self.expandos[k] = self.expandos[k][count_sorted_indexes] + self.expandos[k] = self.expandos[k][count_sorted_indexes] # uses numpy's "fancy indexing" to shuffle in one step if len(self.vectors): - logger.warning("sorting after vectors allocated expensive & error-prone") + logger.warning("sorting after vectors have been allocated is expensive & error-prone") self.vectors = self.vectors[count_sorted_indexes] for i, word in enumerate(self.index_to_key): self.key_to_index[word] = i @@ -625,7 +625,7 @@ def most_similar(self, positive=None, negative=None, topn=10, clip_start=0, clip are searched for most-similar values. For example, restrict_vocab=10000 would only check the first 10000 key vectors in the vocabulary order. (This may be meaningful if you've sorted the vocabulary by descending frequency.) If - specified, overrides any values of clip_start or clip_end + specified, overrides any values of `clip_start` or `clip_end`. Returns ------- @@ -1495,8 +1495,8 @@ def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', (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.) no_header : bool, optional - Default False means a usual word2ve-format file, with a 1st line declaring the count of - following vectors & number of dimensions. If True, the file is assumed lack a declaratory + Default False means a usual word2vec-format file, with a 1st line declaring the count of + following vectors & number of dimensions. If True, the file is assumed to lack a declaratory (vocab_size, vector_size) header and instead start with the 1st vector, and an extra reading-pass will be used to discover the number of vectors. Works only with `binary=False`. Returns From 87860c57e6d772d357c867f050e42cac0072def9 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Mon, 6 Jul 2020 22:42:17 -0700 Subject: [PATCH 45/60] fix cruft left from suggestion --- gensim/models/keyedvectors.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 41310128c5..af76cbc39e 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -379,8 +379,6 @@ def get_vector(self, key, use_norm=False): return result word_vec = get_vector # Compatibility alias for get_vector() - """Compatibility alias for get_vector()""" - return self.get_vector(*args, **kwargs) def add_one(self, key, vector): """Add one new vector at the given key, into existing slot if available. @@ -574,7 +572,7 @@ def sort_by_descending_frequency(self): self.index_to_key = list(np.array(self.index_to_key)[count_sorted_indexes]) self.allocate_vecattrs() for k in self.expandos: - self.expandos[k] = self.expandos[k][count_sorted_indexes] # uses numpy's "fancy indexing" to shuffle in one step + self.expandos[k] = self.expandos[k][count_sorted_indexes] # numpy "fancy indexing" for 1-step shuffle if len(self.vectors): logger.warning("sorting after vectors have been allocated is expensive & error-prone") self.vectors = self.vectors[count_sorted_indexes] From 39fe128cdea9ad1d3592edb7277a49d1af30f681 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 7 Jul 2020 00:18:34 -0700 Subject: [PATCH 46/60] fix numpy-32bit-on-Windows; executable docs --- gensim/models/keyedvectors.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index af76cbc39e..f5849eeeff 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -67,7 +67,7 @@ >>> from gensim.test.utils import lee_corpus_list >>> from gensim.models import Word2Vec >>> - >>> model = Word2Vec(common_texts, size=24, epochs=100) + >>> model = Word2Vec(lee_corpus_list, size=24, epochs=100) >>> word_vectors = model.wv Persist the word vectors to disk with @@ -261,6 +261,8 @@ def allocate_vecattrs(self, attrs=None, types=None): types = [self.expandos[attr].dtype for attr in attrs] target_size = len(self.index_to_key) for attr, t in zip(attrs, types): + if t is int: + t = np.int64 # ensure 'int' type 64-bit (numpy-on-Windows https://github.com/numpy/numpy/issues/9464) if attr not in self.expandos: self.expandos[attr] = np.zeros(target_size, dtype=t) continue From 15152ffee96cb621f199339d3102efe1b03074e1 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 7 Jul 2020 00:48:38 -0700 Subject: [PATCH 47/60] mv lee_corpus to utils; cleanup --- gensim/test/test_word2vec.py | 33 +++++++++++---------------------- gensim/test/utils.py | 11 +++++++++++ 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 84a61c6c30..5589f32f4e 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -20,7 +20,8 @@ from gensim import utils from gensim.models import word2vec, keyedvectors -from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences +from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences, \ + LeeCorpus, lee_corpus_list from testfixtures import log_capture try: @@ -30,15 +31,6 @@ PYEMD_EXT = False -class LeeCorpus(object): - def __iter__(self): - with open(datapath('lee_background.cor')) as f: - for line in f: - yield utils.simple_preprocess(line) - - -list_corpus = list(LeeCorpus()) - new_sentences = [ ['computer', 'artificial', 'intelligence'], ['artificial', 'trees'], @@ -225,7 +217,7 @@ def testOnlineLearningAfterSaveFromFile(self): def onlineSanity(self, model, trained_model=False): terro, others = [], [] - for line in list_corpus: + for line in lee_corpus_list: if 'terrorism' in line: terro.append(line) else: @@ -601,15 +593,15 @@ def model_sanity(self, model, train=True, with_corpus_file=False): """Even tiny models trained on LeeCorpus should pass these sanity checks""" # run extra before/after training tests if train=True if train: - model.build_vocab(list_corpus) + model.build_vocab(lee_corpus_list) orig0 = np.copy(model.wv.vectors[0]) if with_corpus_file: tmpfile = get_tmpfile('gensim_word2vec.tst') - utils.save_as_line_sentence(list_corpus, tmpfile) + utils.save_as_line_sentence(lee_corpus_list, tmpfile) model.train(corpus_file=tmpfile, total_words=model.corpus_total_words, epochs=model.epochs) else: - model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) + model.train(lee_corpus_list, total_examples=model.corpus_count, epochs=model.epochs) self.assertFalse((orig0 == model.wv.vectors[1]).all()) # vector should vary after training sims = model.wv.most_similar('war', topn=len(model.wv.index2word)) t_rank = [word for word, score in sims].index('terrorism') @@ -785,10 +777,7 @@ def testParallel(self): expected_neighbor = 'palestinian' sims = model.wv.most_similar(origin_word, topn=len(model.wv)) # the exact vectors and therefore similarities may differ, due to different thread collisions/randomization - # so let's test only for top3 - from gensim.models.word2vec import FAST_VERSION - print(FAST_VERSION) - print(sims[:20]) + # so let's test only for top10 neighbor_rank = [word for word, sim in sims].index(expected_neighbor) self.assertLess(neighbor_rank, 10) @@ -928,14 +917,14 @@ def _check_old_version(self, old_version): raise ae # check if similarity search and online training works. self.assertTrue(len(model.wv.most_similar('sentence')) == 2) - model.build_vocab(list_corpus, update=True) - model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) + model.build_vocab(lee_corpus_list, update=True) + model.train(lee_corpus_list, total_examples=model.corpus_count, epochs=model.epochs) # check if similarity search and online training works after saving and loading back the model. tmpf = get_tmpfile('gensim_word2vec.tst') model.save(tmpf) loaded_model = word2vec.Word2Vec.load(tmpf) - loaded_model.build_vocab(list_corpus, update=True) - loaded_model.train(list_corpus, total_examples=model.corpus_count, epochs=model.epochs) + loaded_model.build_vocab(lee_corpus_list, update=True) + loaded_model.train(lee_corpus_list, total_examples=model.corpus_count, epochs=model.epochs) @log_capture() def testBuildVocabWarning(self, loglines): diff --git a/gensim/test/utils.py b/gensim/test/utils.py index 1802984e68..ffc402c13d 100644 --- a/gensim/test/utils.py +++ b/gensim/test/utils.py @@ -73,6 +73,7 @@ import shutil from gensim.corpora import Dictionary +from gensim.utils import simple_preprocess module_path = os.path.dirname(__file__) # needed because sample data files are located in the same folder @@ -205,3 +206,13 @@ def temporary_file(name=""): common_dictionary = Dictionary(common_texts) common_corpus = [common_dictionary.doc2bow(text) for text in common_texts] + + +class LeeCorpus(object): + def __iter__(self): + with open(datapath('lee_background.cor')) as f: + for line in f: + yield simple_preprocess(line) + + +lee_corpus_list = list(LeeCorpus()) From 3d424a26ca12c24256d0c58b6179a44fef784743 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 7 Jul 2020 00:59:47 -0700 Subject: [PATCH 48/60] update poincare for latest KV __init__ signature --- gensim/models/poincare.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index ab1fa008b3..b8ba572279 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -50,7 +50,7 @@ import numpy as np from collections import defaultdict, Counter -from numpy import random as np_random +from numpy import random as np_random, float32 as REAL from scipy.stats import spearmanr from six import string_types from six.moves import zip, range @@ -149,7 +149,7 @@ def __init__(self, train_data, size=50, alpha=0.1, negative=10, workers=1, epsil """ self.train_data = train_data - self.kv = PoincareKeyedVectors(size) + self.kv = PoincareKeyedVectors(size, 0) self.all_relations = [] self.node_relations = defaultdict(set) self._negatives_buffer = NegativesBuffer([]) @@ -879,8 +879,8 @@ class PoincareKeyedVectors(KeyedVectors): >>> wv = model.kv.word_vec('kangaroo.n.01') """ - def __init__(self, vector_size): - super(PoincareKeyedVectors, self).__init__(vector_size) + def __init__(self, vector_size, vector_count, dtype=REAL): + super(PoincareKeyedVectors, self).__init__(vector_size, vector_count, dtype=dtype) self.max_distance = 0 def _load_specials(self, *args, **kwargs): From 99f70096f08678356cdc83a8633fcc72ad31f696 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 7 Jul 2020 01:36:13 -0700 Subject: [PATCH 49/60] restore word_vec method for proper overriding, but rm usages --- docs/src/auto_examples/tutorials/run_fasttext.rst | 2 +- docs/src/auto_examples/tutorials/run_word2vec.rst | 2 +- gensim/models/keyedvectors.py | 15 +++++++++------ gensim/models/poincare.py | 12 ++++++------ gensim/test/test_fasttext.py | 14 +++++++------- gensim/test/test_keyedvectors.py | 2 +- 6 files changed, 25 insertions(+), 22 deletions(-) diff --git a/docs/src/auto_examples/tutorials/run_fasttext.rst b/docs/src/auto_examples/tutorials/run_fasttext.rst index 23277ad4c3..1cef50800d 100644 --- a/docs/src/auto_examples/tutorials/run_fasttext.rst +++ b/docs/src/auto_examples/tutorials/run_fasttext.rst @@ -479,7 +479,7 @@ The example training corpus is a toy corpus, results are not expected to be good .. code-block:: none /Volumes/work/workspace/gensim_misha/gensim/models/keyedvectors.py:877: FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future. - vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL) + vectors = vstack(self.get_vector(word, use_norm=True) for word in used_words).astype(REAL) 'breakfast' diff --git a/docs/src/auto_examples/tutorials/run_word2vec.rst b/docs/src/auto_examples/tutorials/run_word2vec.rst index 6bc27f3bf6..67921de03c 100644 --- a/docs/src/auto_examples/tutorials/run_word2vec.rst +++ b/docs/src/auto_examples/tutorials/run_word2vec.rst @@ -308,7 +308,7 @@ Which of the below does not belong in the sequence? .. code-block:: none /home/misha/git/gensim/gensim/models/keyedvectors.py:877: FutureWarning: arrays to stack must be passed as a "sequence" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future. - vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL) + vectors = vstack(self.get_vector(word, use_norm=True) for word in used_words).astype(REAL) car diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index f5849eeeff..23cdc1b4f7 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -139,7 +139,7 @@ >>> vector.shape (100,) >>> - >>> vector = word_vectors.wv.word_vec('office', use_norm=True) + >>> vector = word_vectors.wv.get_vector('office', use_norm=True) >>> vector.shape (100,) @@ -380,7 +380,10 @@ def get_vector(self, key, use_norm=False): result.setflags(write=False) # disallow direct tampering that would invalidate `norms` etc return result - word_vec = get_vector # Compatibility alias for get_vector() + @deprecated("Use get_vector instead") + def word_vec(self, *args, **kwargs): + """Compatibility alias for get_vector(); must exist so subclass calls reach subclass get_vector()""" + return self.get_vector(*args, **kwargs) def add_one(self, key, vector): """Add one new vector at the given key, into existing slot if available. @@ -901,11 +904,11 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): } positive = [ - self.word_vec(word, use_norm=True) if isinstance(word, str) else word + self.get_vector(word, use_norm=True) if isinstance(word, str) else word for word in positive ] negative = [ - self.word_vec(word, use_norm=True) if isinstance(word, str) else word + self.get_vector(word, use_norm=True) if isinstance(word, str) else word for word in negative ] @@ -949,7 +952,7 @@ def rank_by_centrality(self, words, use_norm=True): 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=use_norm) for word in used_words]).astype(REAL) + vectors = vstack([self.get_vector(word, use_norm=use_norm) 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), reverse=True) @@ -1017,7 +1020,7 @@ def distances(self, word_or_vector, other_words=()): """ if isinstance(word_or_vector, KEY_TYPES): - input_vector = self.word_vec(word_or_vector) + input_vector = self.get_vector(word_or_vector) else: input_vector = word_or_vector if not other_words: diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index b8ba572279..1133c52061 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -876,7 +876,7 @@ class PoincareKeyedVectors(KeyedVectors): >>> model.train(epochs=50) >>> >>> # Query the trained model. - >>> wv = model.kv.word_vec('kangaroo.n.01') + >>> wv = model.kv.get_vector('kangaroo.n.01') """ def __init__(self, vector_size, vector_count, dtype=REAL): @@ -1067,8 +1067,8 @@ def distance(self, w1, w2): If either of `w1` and `w2` is absent from vocab. """ - vector_1 = self.word_vec(w1) - vector_2 = self.word_vec(w2) + vector_1 = self.get_vector(w1) + vector_2 = self.get_vector(w2) return self.vector_distance(vector_1, vector_2) def similarity(self, w1, w2): @@ -1215,7 +1215,7 @@ def distances(self, node_or_vector, other_nodes=()): """ if isinstance(node_or_vector, string_types): - input_vector = self.word_vec(node_or_vector) + input_vector = self.get_vector(node_or_vector) else: input_vector = node_or_vector if not other_nodes: @@ -1260,7 +1260,7 @@ def norm(self, node_or_vector): """ if isinstance(node_or_vector, string_types): - input_vector = self.word_vec(node_or_vector) + input_vector = self.get_vector(node_or_vector) else: input_vector = node_or_vector return np.linalg.norm(input_vector) @@ -1676,7 +1676,7 @@ def score_function(self, embedding, trie, term_1, term_2): min_term_1, min_term_2 = term_1, term_2 min_distance = distance assert min_term_1 is not None and min_term_2 is not None - vector_1, vector_2 = embedding.word_vec(min_term_1), embedding.word_vec(min_term_2) + vector_1, vector_2 = embedding.get_vector(min_term_1), embedding.get_vector(min_term_2) norm_1, norm_2 = np.linalg.norm(vector_1), np.linalg.norm(vector_2) return -1 * (1 + self.alpha * (norm_2 - norm_1)) * min_distance diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index 2a492dacc3..73f004f4bb 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -945,7 +945,7 @@ def test_in_vocab(self): expected = dict(load_vec(fin)) for word, expected_vector in expected.items(): - actual_vector = native.wv.word_vec(word) + actual_vector = native.wv.get_vector(word) self.assertTrue(np.allclose(expected_vector, actual_vector, atol=1e-5)) self.model_structural_sanity(native) @@ -955,7 +955,7 @@ def test_out_of_vocab(self): native = load_native() for word, expected_vector in self.oov_expected.items(): - actual_vector = native.wv.word_vec(word) + actual_vector = native.wv.get_vector(word) self.assertTrue(np.allclose(expected_vector, actual_vector, atol=1e-5)) self.model_structural_sanity(native) @@ -971,7 +971,7 @@ def test_out_of_vocab_gensim(self): model = train_gensim() for word, expected_vector in self.oov_expected.items(): - actual_vector = model.wv.word_vec(word) + actual_vector = model.wv.get_vector(word) self.assertTrue(np.allclose(expected_vector, actual_vector, atol=1e-5)) self.model_structural_sanity(model) @@ -1004,11 +1004,11 @@ def test_continuation_native(self): # Its vectors should be different between training runs. # word = 'human' # FIXME: this isn't actually in model, except via OOV ngrams - old_vector = native.wv.word_vec(word).tolist() + old_vector = native.wv.get_vector(word).tolist() native.train(list_corpus, total_examples=len(list_corpus), epochs=native.epochs) - new_vector = native.wv.word_vec(word).tolist() + new_vector = native.wv.get_vector(word).tolist() self.assertNotEqual(old_vector, new_vector) self.model_structural_sanity(native) @@ -1019,13 +1019,13 @@ def test_continuation_gensim(self): vectors_ngrams_before = np.copy(model.wv.vectors_ngrams) word = 'human' - old_vector = model.wv.word_vec(word).tolist() + old_vector = model.wv.get_vector(word).tolist() model.train(list_corpus, total_examples=len(list_corpus), epochs=model.epochs) vectors_ngrams_after = np.copy(model.wv.vectors_ngrams) self.assertFalse(np.allclose(vectors_ngrams_before, vectors_ngrams_after)) - new_vector = model.wv.word_vec(word).tolist() + new_vector = model.wv.get_vector(word).tolist() self.assertNotEqual(old_vector, new_vector) self.model_structural_sanity(model) diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py index c5054bd735..f3e9329f75 100644 --- a/gensim/test/test_keyedvectors.py +++ b/gensim/test/test_keyedvectors.py @@ -299,7 +299,7 @@ class Gensim320Test(unittest.TestCase): def test(self): path = datapath('old_keyedvectors_320.dat') vectors = gensim.models.keyedvectors.KeyedVectors.load(path) - self.assertTrue(vectors.word_vec('computer') is not None) + self.assertTrue(vectors.get_vector('computer') is not None) def save_dict_to_word2vec_formated_file(fname, word2vec_dict): From 2bb8abfdf3152b23cb0a4db1cb624694925baed5 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 7 Jul 2020 02:14:04 -0700 Subject: [PATCH 50/60] Apply suggestions from code review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Radim Řehůřek --- gensim/models/fasttext_inner.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index 36ccdb91b9..097a61d02e 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -8,7 +8,7 @@ """Optimized Cython functions for training a :class:`~gensim.models.fasttext.FastText` model. The main entry point is :func:`~gensim.models.fasttext_inner.train_batch_any` -which may be called directly from Python code. +which may be called directly from Python code. Notes ----- From 33c6508fac574418a490118a1356e03a92deb20e Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 7 Jul 2020 22:55:24 -0700 Subject: [PATCH 51/60] adjust testParallel against failure risk --- gensim/test/test_word2vec.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index 5589f32f4e..c5c88a3888 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -772,14 +772,14 @@ def testParallel(self): corpus = utils.RepeatCorpus(LeeCorpus(), 10000) # repeats about 33 times for workers in [4, ]: # [4, 2] - model = word2vec.Word2Vec(corpus, vector_size=12, min_count=(5 * 33), workers=workers) + model = word2vec.Word2Vec(corpus, vector_size=16, min_count=(10 * 33), workers=workers) origin_word = 'israeli' expected_neighbor = 'palestinian' sims = model.wv.most_similar(origin_word, topn=len(model.wv)) # the exact vectors and therefore similarities may differ, due to different thread collisions/randomization # so let's test only for top10 neighbor_rank = [word for word, sim in sims].index(expected_neighbor) - self.assertLess(neighbor_rank, 10) + self.assertLess(neighbor_rank, 20) def testRNG(self): """Test word2vec results identical with identical RNG seed.""" From cb33e467ae80a9912733b46f6b660fd5a64ad047 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Fri, 10 Jul 2020 18:48:52 -0700 Subject: [PATCH 52/60] intensify training for an occasionally failing test --- gensim/test/test_word2vec.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/test/test_word2vec.py b/gensim/test/test_word2vec.py index c5c88a3888..1be1ea9d21 100644 --- a/gensim/test/test_word2vec.py +++ b/gensim/test/test_word2vec.py @@ -248,7 +248,7 @@ def test_cbow_hs_online(self): """Test CBOW w/ hierarchical softmax""" model = word2vec.Word2Vec( sg=0, cbow_mean=1, alpha=0.05, window=5, hs=1, negative=0, - min_count=3, epochs=10, seed=42, workers=2 + min_count=3, epochs=20, seed=42, workers=2 ) self.onlineSanity(model) From 581ef0666c48a2351f8c8878b602298aa3cc16ac Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Tue, 14 Jul 2020 00:00:02 -0700 Subject: [PATCH 53/60] clarify word/char ngrams handling; rm outdated comments --- gensim/models/fasttext.py | 72 ++++++++++++++---------------------- gensim/test/test_fasttext.py | 8 ++-- 2 files changed, 32 insertions(+), 48 deletions(-) diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py index 117c0a3aff..5c07a0b540 100644 --- a/gensim/models/fasttext.py +++ b/gensim/models/fasttext.py @@ -406,12 +406,14 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100 max_n : int, optional Max length of char ngrams to be used for training word representations. Set `max_n` to be lesser than `min_n` to avoid char ngrams being used. - word_ngrams : {1,0}, optional - If 1, uses enriches word vectors with subword(n-grams) information. - If 0, this is equivalent to :class:`~gensim.models.word2vec.Word2Vec`. + word_ngrams : int, optional + In Facebook's FastText, "max length of word ngram" - but gensim only supports the + default of 1 (regular unigram word handling). bucket : int, optional Character ngrams are hashed into a fixed number of buckets, in order to limit the memory usage of the model. This option specifies the number of buckets used by the model. + The default value of 2000000 consumes as much memory as having 2000000 more in-vocabulary + words in your model. callbacks : :obj: `list` of :obj: `~gensim.models.callbacks.CallbackAny2Vec`, optional List of callbacks that need to be executed/run at specific stages during training. max_final_vocab : int, optional @@ -442,26 +444,16 @@ def __init__(self, sentences=None, corpus_file=None, sg=0, hs=0, vector_size=100 words (that do not exist in the vocabulary), as the aggregate of the n-grams included in the word. After training the model, this attribute can be used directly to query those embeddings in various ways. Check the module level docstring for some examples. - vocabulary : :class:`~gensim.models.fasttext.FastTextVocab` - This object represents the vocabulary of the model. - Besides keeping track of all unique words, this object provides extra functionality, such as - constructing a huffman tree (frequent words are closer to the root), or discarding extremely rare words. - trainables : :class:`~gensim.models.fasttext.FastTextTrainables` - This object represents the inner shallow neural network used to train the embeddings. This is very - similar to the network of the :class:`~gensim.models.word2vec.Word2Vec` model, but it also trains weights - for the N-Grams (sequences of more than 1 words). The semantics of the network are almost the same as - the one used for the :class:`~gensim.models.word2vec.Word2Vec` model. - You can think of it as a NN with a single projection and hidden layer which we train on the corpus. - The weights are then used as our embeddings. An important difference however between the two models, is the - scoring function used to compute the loss. In the case of FastText, this is modified in word to also account - for the internal structure of words, besides their concurrence counts. """ self.load = call_on_class_only self.load_fasttext_format = call_on_class_only self.callbacks = callbacks - self.word_ngrams = int(word_ngrams) - if self.word_ngrams <= 1 and max_n == 0: + if word_ngrams != 1: + raise NotImplementedError("Gensim's FastText implementation does not yet support word_ngrams != 1.") + self.word_ngrams = word_ngrams + if max_n < min_n: + # with no eligible char-ngram lengths, no buckets need be allocated bucket = 0 self.wv = FastTextKeyedVectors(vector_size, min_n, max_n, bucket) @@ -615,26 +607,16 @@ def estimate_memory(self, vocab_size=None, report=None): report['syn1'] = len(self.wv) * l1_size if self.negative: report['syn1neg'] = len(self.wv) * l1_size - if self.word_ngrams > 0 and len(self.wv): - num_buckets = num_ngrams = 0 - - if self.wv.bucket: - buckets = set() - num_ngrams = 0 - for word in self.wv.key_to_index: - hashes = ft_ngram_hashes(word, self.wv.min_n, self.wv.max_n, self.wv.bucket) - num_ngrams += len(hashes) - buckets.update(hashes) - num_buckets = len(buckets) - report['syn0_ngrams'] = num_buckets * vec_size - # A tuple (48 bytes) with num_ngrams_word ints (8 bytes) for each word + if self.wv.bucket: + report['syn0_ngrams'] = self.wv.bucket * vec_size + num_ngrams = 0 + for word in self.wv.key_to_index: + hashes = ft_ngram_hashes(word, self.wv.min_n, self.wv.max_n, self.wv.bucket) + num_ngrams += len(hashes) + # A list (64 bytes) with one np.array (100 bytes) per key, with a total of + # num_ngrams uint32s (4 bytes) amongst them # Only used during training, not stored with the model - report['buckets_word'] = 48 * len(self.wv) + 8 * num_ngrams # FIXME: this looks confused -gojomo - elif self.word_ngrams > 0: - logger.warning( - 'Subword information is enabled, but no vocabulary could be found. ' - 'Estimated required memory might be inaccurate!', - ) + report['buckets_word'] = 64 + (100 * len(self.wv)) + (4 * num_ngrams) # FIXME: caching & calc sensible? report['total'] = sum(report.values()) logger.info( "estimated required memory for %i words, %i buckets and %i dimensions: %i bytes", @@ -879,7 +861,7 @@ def load(cls, *args, **kwargs): if hasattr(model, 'bucket'): del model.bucket # should only exist in one place: the wv subcomponent if not hasattr(model.wv, 'buckets_word') or not model.wv.buckets_word: - model.wv.recalc_word_ngram_buckets() + model.wv.recalc_char_ngram_buckets() return model @@ -1195,8 +1177,8 @@ def __init__(self, vector_size, min_n, max_n, bucket): A vector for each ngram across all entities in the vocabulary. Each row is a vector that corresponds to a bucket. Columns correspond to vector dimensions. - buckets_word : dict - Maps vocabulary items (by their index) to the buckets they occur in. + buckets_word : list of np.array + For each key (by its index), report bucket slots their subwords map to. When used in training, FastTextKeyedVectors may be decorated with extra attributes that closely associate with its core attributes, @@ -1339,7 +1321,7 @@ def init_ngrams_weights(self, seed): Call this **after** the vocabulary has been fully initialized. """ - self.recalc_word_ngram_buckets() + self.recalc_char_ngram_buckets() rand_obj = np.random.default_rng(seed=seed) # use new instance of numpy's recommended generator/algorithm @@ -1374,7 +1356,7 @@ def update_ngrams_weights(self, seed, old_vocab_len): Call this **after** the vocabulary has been updated. """ - self.recalc_word_ngram_buckets() + self.recalc_char_ngram_buckets() rand_obj = np.random rand_obj.seed(seed) @@ -1407,7 +1389,7 @@ def init_post_load(self, fb_vectors): # self.vectors_vocab = np.array(fb_vectors[:vocab_words, :]) self.vectors_ngrams = np.array(fb_vectors[vocab_words:, :]) - self.recalc_word_ngram_buckets() + self.recalc_char_ngram_buckets() self.adjust_vectors() # calculate composite full-word vectors def adjust_vectors(self): @@ -1428,7 +1410,7 @@ def adjust_vectors(self): self.vectors[i] += self.vectors_ngrams[nh] self.vectors[i] /= len(ngram_buckets) + 1 - def recalc_word_ngram_buckets(self): + def recalc_char_ngram_buckets(self): """ Scan the vocabulary, calculate ngrams and their hashes, and cache the list of ngrams for each known word. @@ -1444,7 +1426,7 @@ def recalc_word_ngram_buckets(self): self.buckets_word[i] = np.array( ft_ngram_hashes(word, self.min_n, self.max_n, self.bucket), dtype=np.uint32, - ) + ) def _pad_random(m, new_rows, rand): diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index 73f004f4bb..b9a744778e 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -803,9 +803,11 @@ def test_estimate_memory(self): self.assertEqual(report['syn0_vocab'], 192) self.assertEqual(report['syn1'], 192) self.assertEqual(report['syn1neg'], 192) - self.assertEqual(report['syn0_ngrams'], 2688) - self.assertEqual(report['buckets_word'], 640) - self.assertEqual(report['total'], 6704) + # FIXME: these fixed numbers for particular implementation generations encumber changes without real QA + # perhaps instead verify reports' total is within some close factor of a deep-audit of actual memory used? + self.assertEqual(report['syn0_ngrams'], model.vector_size * np.dtype(np.float32).itemsize * BUCKET) + self.assertEqual(report['buckets_word'], 688) + self.assertEqual(report['total'], 484064) def obsolete_testLoadOldModel(self): """Test loading fasttext models from previous version""" From 9f21cbae8239a877e6d55b0e55ddfd5c86297be9 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 16 Jul 2020 13:32:23 -0700 Subject: [PATCH 54/60] mostly avoid duplciating FastTextConfig fields into locals --- gensim/models/fasttext_inner.pyx | 231 +++++++++++-------------------- 1 file changed, 84 insertions(+), 147 deletions(-) diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index 097a61d02e..72d4f393e6 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -91,43 +91,22 @@ cdef void fasttext_fast_sentence_sg_neg(FastTextConfig *c, int i, int j) nogil: """ - # - # Unpack the struct, extracting only the required parts into separate - # variables. This is here for historical reasons. We could bypass these - # declarations and use parts of the struct directly, but that would be - # somewhat more verbose. - # cdef: - int negative = c.negative - np.uint32_t *cum_table = c.cum_table - unsigned long long cum_table_len = c.cum_table_len - REAL_t *syn0_vocab = c.syn0_vocab - REAL_t *syn0_ngrams = c.syn0_ngrams - REAL_t *syn1neg = c.syn1neg - int size = c.size np.uint32_t word_index = c.indexes[j] np.uint32_t word2_index = c.indexes[i] np.uint32_t *subwords_index = c.subwords_idx[i] np.uint32_t subwords_len = c.subwords_idx_len[i] - REAL_t alpha = c.alpha - REAL_t *work = c.work - REAL_t *l1 = c.neu1 - unsigned long long next_random = c.next_random - REAL_t *vocab_lockf = c.vocab_lockf - np.uint32_t vocab_lockf_len = c.vocab_lockf_len - REAL_t *ngrams_lockf = c.ngrams_lockf - np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len - - cdef long long row1 = word2_index * size, row2 + + cdef long long row1 = word2_index * c.size, row2 cdef unsigned long long modulo = 281474976710655ULL cdef REAL_t f, g, label, f_dot cdef np.uint32_t target_index cdef int d - memset(work, 0, size * cython.sizeof(REAL_t)) - memset(l1, 0, size * cython.sizeof(REAL_t)) + memset(c.work, 0, c.size * cython.sizeof(REAL_t)) + memset(c.neu1, 0, c.size * cython.sizeof(REAL_t)) - scopy(&size, &syn0_vocab[row1], &ONE, l1, &ONE) + scopy(&c.size, &c.syn0_vocab[row1], &ONE, c.neu1, &ONE) # # Avoid division by zero. @@ -135,35 +114,34 @@ cdef void fasttext_fast_sentence_sg_neg(FastTextConfig *c, int i, int j) nogil: cdef REAL_t norm_factor if subwords_len: for d in range(subwords_len): - our_saxpy(&size, &ONEF, &syn0_ngrams[subwords_index[d] * size], &ONE, l1, &ONE) + our_saxpy(&c.size, &ONEF, &c.syn0_ngrams[subwords_index[d] * c.size], &ONE, c.neu1, &ONE) norm_factor = ONEF / subwords_len - sscal(&size, &norm_factor, l1 , &ONE) + sscal(&c.size, &norm_factor, c.neu1, &ONE) - for d in range(negative+1): + for d in range(c.negative+1): if d == 0: target_index = word_index label = ONEF else: - target_index = bisect_left(cum_table, (next_random >> 16) % cum_table[cum_table_len-1], 0, cum_table_len) - next_random = (next_random * 25214903917ULL + 11) & modulo + target_index = bisect_left( + c.cum_table, (c.next_random >> 16) % c.cum_table[c.cum_table_len-1], 0, c.cum_table_len) + c.next_random = (c.next_random * 25214903917ULL + 11) & modulo if target_index == word_index: continue label = 0.0 - row2 = target_index * size - f_dot = our_dot(&size, l1, &ONE, &syn1neg[row2], &ONE) + row2 = target_index * c.size + f_dot = our_dot(&c.size, c.neu1, &ONE, &c.syn1neg[row2], &ONE) if f_dot <= -MAX_EXP or f_dot >= MAX_EXP: continue f = EXP_TABLE[((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))] - g = (label - f) * alpha - our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE) - our_saxpy(&size, &g, l1, &ONE, &syn1neg[row2], &ONE) - our_saxpy(&size, &vocab_lockf[word2_index % vocab_lockf_len], work, &ONE, &syn0_vocab[row1], &ONE) + g = (label - f) * c.alpha + our_saxpy(&c.size, &g, &c.syn1neg[row2], &ONE, c.work, &ONE) + our_saxpy(&c.size, &g, c.neu1, &ONE, &c.syn1neg[row2], &ONE) + our_saxpy(&c.size, &c.vocab_lockf[word2_index % c.vocab_lockf_len], c.work, &ONE, &c.syn0_vocab[row1], &ONE) for d in range(subwords_len): - our_saxpy(&size, &ngrams_lockf[subwords_index[d] % ngrams_lockf_len], - work, &ONE, &syn0_ngrams[subwords_index[d]*size], &ONE) - - c.next_random = next_random + our_saxpy(&c.size, &c.ngrams_lockf[subwords_index[d] % c.ngrams_lockf_len], + c.work, &ONE, &c.syn0_ngrams[subwords_index[d]*c.size], &ONE) cdef void fasttext_fast_sentence_sg_hs(FastTextConfig *c, int i, int j) nogil: @@ -185,20 +163,9 @@ cdef void fasttext_fast_sentence_sg_hs(FastTextConfig *c, int i, int j) nogil: np.uint32_t *word_point = c.points[j] np.uint8_t *word_code = c.codes[j] int codelen = c.codelens[j] - REAL_t *syn0_vocab = c.syn0_vocab - REAL_t *syn0_ngrams = c.syn0_ngrams - REAL_t *syn1 = c.syn1 - int size = c.size np.uint32_t word2_index = c.indexes[i] np.uint32_t *subwords_index = c.subwords_idx[i] np.uint32_t subwords_len = c.subwords_idx_len[i] - REAL_t alpha = c.alpha - REAL_t *work = c.work - REAL_t *l1 = c.neu1 - REAL_t *vocab_lockf = c.vocab_lockf - np.uint32_t vocab_lockf_len = c.vocab_lockf_len - REAL_t *ngrams_lockf = c.ngrams_lockf - np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len # # b : long long @@ -215,13 +182,13 @@ cdef void fasttext_fast_sentence_sg_hs(FastTextConfig *c, int i, int j) nogil: # ? # cdef long long b - cdef long long row1 = word2_index * size, row2 + cdef long long row1 = word2_index * c.size, row2 cdef REAL_t f, g, f_dot - memset(work, 0, size * cython.sizeof(REAL_t)) - memset(l1, 0, size * cython.sizeof(REAL_t)) + memset(c.work, 0, c.size * cython.sizeof(REAL_t)) + memset(c.neu1, 0, c.size * cython.sizeof(REAL_t)) - scopy(&size, &syn0_vocab[row1], &ONE, l1, &ONE) + scopy(&c.size, &c.syn0_vocab[row1], &ONE, c.neu1, &ONE) # # Avoid division by zero. @@ -229,26 +196,28 @@ cdef void fasttext_fast_sentence_sg_hs(FastTextConfig *c, int i, int j) nogil: cdef REAL_t norm_factor if subwords_len: for d in range(subwords_len): - row2 = subwords_index[d] * size - our_saxpy(&size, &ONEF, &syn0_ngrams[row2], &ONE, l1, &ONE) + row2 = subwords_index[d] * c.size + our_saxpy(&c.size, &ONEF, &c.syn0_ngrams[row2], &ONE, c.neu1, &ONE) norm_factor = ONEF / subwords_len - sscal(&size, &norm_factor, l1 , &ONE) + sscal(&c.size, &norm_factor, c.neu1, &ONE) for b in range(codelen): - row2 = word_point[b] * size - f_dot = our_dot(&size, l1, &ONE, &syn1[row2], &ONE) + row2 = word_point[b] * c.size + f_dot = our_dot(&c.size, c.neu1, &ONE, &c.syn1[row2], &ONE) if f_dot <= -MAX_EXP or f_dot >= MAX_EXP: continue f = EXP_TABLE[((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))] - g = (1 - word_code[b] - f) * alpha + g = (1 - word_code[b] - f) * c.alpha - our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE) - our_saxpy(&size, &g, l1, &ONE, &syn1[row2], &ONE) + our_saxpy(&c.size, &g, &c.syn1[row2], &ONE, c.work, &ONE) + our_saxpy(&c.size, &g, c.neu1, &ONE, &c.syn1[row2], &ONE) - our_saxpy(&size, &vocab_lockf[word2_index % vocab_lockf_len], work, &ONE, &syn0_vocab[row1], &ONE) + our_saxpy(&c.size, &c.vocab_lockf[word2_index % c.vocab_lockf_len], c.work, &ONE, &c.syn0_vocab[row1], &ONE) for d in range(subwords_len): - row2 = subwords_index[d] * size - our_saxpy(&size, &ngrams_lockf[subwords_index[d] % ngrams_lockf_len], work, &ONE, &syn0_ngrams[row2], &ONE) + row2 = subwords_index[d] * c.size + our_saxpy( + &c.size, &c.ngrams_lockf[subwords_index[d] % c.ngrams_lockf_len], c.work, &ONE, + &c.syn0_ngrams[row2], &ONE) cdef void fasttext_fast_sentence_cbow_neg(FastTextConfig *c, int i, int j, int k) nogil: @@ -271,89 +240,69 @@ cdef void fasttext_fast_sentence_cbow_neg(FastTextConfig *c, int i, int j, int k """ - cdef: - int negative = c.negative - np.uint32_t *cum_table = c.cum_table - unsigned long long cum_table_len = c.cum_table_len - # int *codelens = c.codelens - REAL_t *neu1 = c.neu1 - REAL_t *syn0_vocab = c.syn0_vocab - REAL_t *syn0_ngrams = c.syn0_ngrams - REAL_t *syn1neg = c.syn1neg - int size = c.size - np.uint32_t *indexes = c.indexes - np.uint32_t **subwords_idx = c.subwords_idx - int *subwords_idx_len = c.subwords_idx_len - REAL_t alpha = c.alpha - REAL_t *work = c.work - int cbow_mean = c.cbow_mean - unsigned long long next_random = c.next_random - REAL_t *vocab_lockf = c.vocab_lockf - np.uint32_t vocab_lockf_len = c.vocab_lockf_len - REAL_t *ngrams_lockf = c.ngrams_lockf - np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len - cdef long long row2 cdef unsigned long long modulo = 281474976710655ULL cdef REAL_t f, g, count, inv_count = 1.0, label, f_dot cdef np.uint32_t target_index, word_index cdef int d, m - word_index = indexes[i] + word_index = c.indexes[i] - memset(neu1, 0, size * cython.sizeof(REAL_t)) + memset(c.neu1, 0, c.size * cython.sizeof(REAL_t)) count = 0.0 for m in range(j, k): if m == i: continue count += ONEF - our_saxpy(&size, &ONEF, &syn0_vocab[indexes[m] * size], &ONE, neu1, &ONE) - for d in range(subwords_idx_len[m]): + our_saxpy(&c.size, &ONEF, &c.syn0_vocab[c.indexes[m] * c.size], &ONE, c.neu1, &ONE) + for d in range(c.subwords_idx_len[m]): count += ONEF - our_saxpy(&size, &ONEF, &syn0_ngrams[subwords_idx[m][d] * size], &ONE, neu1, &ONE) + our_saxpy(&c.size, &ONEF, &c.syn0_ngrams[c.subwords_idx[m][d] * c.size], &ONE, c.neu1, &ONE) if count > (0.5): inv_count = ONEF / count - if cbow_mean: - sscal(&size, &inv_count, neu1, &ONE) + if c.cbow_mean: + sscal(&c.size, &inv_count, c.neu1, &ONE) - memset(work, 0, size * cython.sizeof(REAL_t)) + memset(c.work, 0, c.size * cython.sizeof(REAL_t)) - for d in range(negative+1): + for d in range(c.negative+1): if d == 0: target_index = word_index label = ONEF else: - target_index = bisect_left(cum_table, (next_random >> 16) % cum_table[cum_table_len-1], 0, cum_table_len) - next_random = (next_random * 25214903917ULL + 11) & modulo + target_index = bisect_left(c.cum_table, (c.next_random >> 16) % c.cum_table[c.cum_table_len-1], 0, c.cum_table_len) + c.next_random = (c.next_random * 25214903917ULL + 11) & modulo if target_index == word_index: continue label = 0.0 - row2 = target_index * size - f_dot = our_dot(&size, neu1, &ONE, &syn1neg[row2], &ONE) + row2 = target_index * c.size + f_dot = our_dot(&c.size, c.neu1, &ONE, &c.syn1neg[row2], &ONE) if f_dot <= -MAX_EXP: f = 0.0 elif f_dot >= MAX_EXP: f = 1.0 else: f = EXP_TABLE[((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))] - g = (label - f) * alpha + g = (label - f) * c.alpha - our_saxpy(&size, &g, &syn1neg[row2], &ONE, work, &ONE) - our_saxpy(&size, &g, neu1, &ONE, &syn1neg[row2], &ONE) + our_saxpy(&c.size, &g, &c.syn1neg[row2], &ONE, c.work, &ONE) + our_saxpy(&c.size, &g, c.neu1, &ONE, &c.syn1neg[row2], &ONE) - if not cbow_mean: # divide error over summed window vectors - sscal(&size, &inv_count, work, &ONE) + if not c.cbow_mean: # divide error over summed window vectors + sscal(&c.size, &inv_count, c.work, &ONE) for m in range(j,k): if m == i: continue - our_saxpy(&size, &vocab_lockf[indexes[m] % vocab_lockf_len], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE) - for d in range(subwords_idx_len[m]): - our_saxpy(&size, &ngrams_lockf[subwords_idx[m][d] % ngrams_lockf_len], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE) - - c.next_random = next_random + our_saxpy( + &c.size, &c.vocab_lockf[c.indexes[m] % c.vocab_lockf_len], c.work, &ONE, + &c.syn0_vocab[c.indexes[m]*c.size], &ONE) + for d in range(c.subwords_idx_len[m]): + our_saxpy( + &c.size, &c.ngrams_lockf[c.subwords_idx[m][d] % c.ngrams_lockf_len], c.work, &ONE, + &c.syn0_ngrams[c.subwords_idx[m][d]*c.size], &ONE) cdef void fasttext_fast_sentence_cbow_hs(FastTextConfig *c, int i, int j, int k) nogil: @@ -375,64 +324,52 @@ cdef void fasttext_fast_sentence_cbow_hs(FastTextConfig *c, int i, int j, int k) cdef: np.uint32_t *word_point = c.points[i] np.uint8_t *word_code = c.codes[i] - int *codelens = c.codelens - REAL_t *neu1 = c.neu1 - REAL_t *syn0_vocab = c.syn0_vocab - REAL_t *syn0_ngrams = c.syn0_ngrams - REAL_t *syn1 = c.syn1 - int size = c.size - np.uint32_t *indexes = c.indexes - np.uint32_t **subwords_idx = c.subwords_idx - int *subwords_idx_len = c.subwords_idx_len - REAL_t alpha = c.alpha - REAL_t *work = c.work - int cbow_mean = c.cbow_mean - REAL_t *vocab_lockf = c.vocab_lockf - np.uint32_t vocab_lockf_len = c.vocab_lockf_len - REAL_t *ngrams_lockf = c.ngrams_lockf - np.uint32_t ngrams_lockf_len = c.ngrams_lockf_len cdef long long b cdef long long row2 cdef REAL_t f, g, count, inv_count = 1.0, f_dot cdef int m - memset(neu1, 0, size * cython.sizeof(REAL_t)) + memset(c.neu1, 0, c.size * cython.sizeof(REAL_t)) count = 0.0 for m in range(j, k): if m == i: continue count += ONEF - our_saxpy(&size, &ONEF, &syn0_vocab[indexes[m] * size], &ONE, neu1, &ONE) - for d in range(subwords_idx_len[m]): + our_saxpy(&c.size, &ONEF, &c.syn0_vocab[c.indexes[m] * c.size], &ONE, c.neu1, &ONE) + for d in range(c.subwords_idx_len[m]): count += ONEF - our_saxpy(&size, &ONEF, &syn0_ngrams[subwords_idx[m][d] * size], &ONE, neu1, &ONE) + our_saxpy(&c.size, &ONEF, &c.syn0_ngrams[c.subwords_idx[m][d] * c.size], &ONE, c.neu1, &ONE) if count > (0.5): inv_count = ONEF / count - if cbow_mean: - sscal(&size, &inv_count, neu1, &ONE) + if c.cbow_mean: + sscal(&c.size, &inv_count, c.neu1, &ONE) - memset(work, 0, size * cython.sizeof(REAL_t)) - for b in range(codelens[i]): - row2 = word_point[b] * size - f_dot = our_dot(&size, neu1, &ONE, &syn1[row2], &ONE) + memset(c.work, 0, c.size * cython.sizeof(REAL_t)) + for b in range(c.codelens[i]): + row2 = word_point[b] * c.size + f_dot = our_dot(&c.size, c.neu1, &ONE, &c.syn1[row2], &ONE) if f_dot <= -MAX_EXP or f_dot >= MAX_EXP: continue f = EXP_TABLE[((f_dot + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))] - g = (1 - word_code[b] - f) * alpha + g = (1 - word_code[b] - f) * c.alpha - our_saxpy(&size, &g, &syn1[row2], &ONE, work, &ONE) - our_saxpy(&size, &g, neu1, &ONE, &syn1[row2], &ONE) + our_saxpy(&c.size, &g, &c.syn1[row2], &ONE, c.work, &ONE) + our_saxpy(&c.size, &g, c.neu1, &ONE, &c.syn1[row2], &ONE) - if not cbow_mean: # divide error over summed window vectors - sscal(&size, &inv_count, work, &ONE) + if not c.cbow_mean: # divide error over summed window vectors + sscal(&c.size, &inv_count, c.work, &ONE) for m in range(j,k): if m == i: continue - our_saxpy(&size, &vocab_lockf[indexes[m] % vocab_lockf_len], work, &ONE, &syn0_vocab[indexes[m]*size], &ONE) - for d in range(subwords_idx_len[m]): - our_saxpy(&size, &ngrams_lockf[subwords_idx[m][d] % ngrams_lockf_len], work, &ONE, &syn0_ngrams[subwords_idx[m][d]*size], &ONE) + our_saxpy( + &c.size, &c.vocab_lockf[c.indexes[m] % c.vocab_lockf_len], c.work, &ONE, + &c.syn0_vocab[c.indexes[m]*c.size], &ONE) + for d in range(c.subwords_idx_len[m]): + our_saxpy( + &c.size, &c.ngrams_lockf[c.subwords_idx[m][d] % c.ngrams_lockf_len], c.work, &ONE, + &c.syn0_ngrams[c.subwords_idx[m][d]*c.size], &ONE) cdef void init_ft_config(FastTextConfig *c, model, alpha, _work, _neu1): @@ -627,7 +564,7 @@ cdef void fasttext_train_any(FastTextConfig *c, int num_sentences) nogil: def train_batch_any(model, sentences, alpha, _work, _neu1): - """Update the CBOW model by training on a sequence of sentences. + """Update the model by training on a sequence of sentences. Each sentence is a list of string tokens, which are looked up in the model's vocab dictionary. Called internally from :meth:`~gensim.models.fasttext.FastText.train`. From d9126168f5dab1d97086951564380787dd511ce9 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 16 Jul 2020 13:33:46 -0700 Subject: [PATCH 55/60] avoid copies/pointers for no-bucket (FT as W2V) case --- gensim/models/fasttext_inner.pyx | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index 72d4f393e6..c4fa3d9fd9 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -483,8 +483,11 @@ cdef object populate_ft_config(FastTextConfig *c, wv, buckets_word, sentences): continue c.indexes[effective_words] = word_index - c.subwords_idx_len[effective_words] = (len(buckets_word[word_index])) - c.subwords_idx[effective_words] = np.PyArray_DATA(buckets_word[word_index]) + if wv.bucket: + c.subwords_idx_len[effective_words] = (len(buckets_word[word_index])) + c.subwords_idx[effective_words] = np.PyArray_DATA(buckets_word[word_index]) + else: + c.subwords_idx_len[effective_words] = 0 if c.hs: c.codelens[effective_words] = len(vocab_codes[word_index]) From 583bbe6138a9b6974c8c14fd9b91387e8e22f2ad Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 16 Jul 2020 13:35:01 -0700 Subject: [PATCH 56/60] rm obsolete test (already skipped & somewhat originally misguided) --- gensim/test/test_fasttext.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py index b9a744778e..c8c9b0582c 100644 --- a/gensim/test/test_fasttext.py +++ b/gensim/test/test_fasttext.py @@ -962,22 +962,6 @@ def test_out_of_vocab(self): self.model_structural_sanity(native) - @unittest.skip('this test does not pass currently, I suspect a bug in our FT implementation') - def test_out_of_vocab_gensim(self): - """Test whether gensim gives similar results to FB for OOV words. - - Seems to be broken for our toy model. - - # GM: probably unreasonable to expect identical results given alg randomization & thread jitter - """ - model = train_gensim() - - for word, expected_vector in self.oov_expected.items(): - actual_vector = model.wv.get_vector(word) - self.assertTrue(np.allclose(expected_vector, actual_vector, atol=1e-5)) - - self.model_structural_sanity(model) - def test_sanity(self): """Compare models trained on toy data. They should be equal.""" trained = train_gensim() From 0330cfc22fb21f01cdb2200d2e8bc770ccb97829 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 16 Jul 2020 14:01:23 -0700 Subject: [PATCH 57/60] simpler/faster .get(..., default) (avoids exception-catching in has_index_for) --- gensim/models/fasttext_inner.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/fasttext_inner.pyx b/gensim/models/fasttext_inner.pyx index c4fa3d9fd9..e71ed6f31d 100644 --- a/gensim/models/fasttext_inner.pyx +++ b/gensim/models/fasttext_inner.pyx @@ -476,7 +476,7 @@ cdef object populate_ft_config(FastTextConfig *c, wv, buckets_word, sentences): if not sent: continue # ignore empty sentences; leave effective_sentences unchanged for token in sent: - word_index = wv.key_to_index[token] if wv.has_index_for(token) else None + word_index = wv.key_to_index.get(token, None) if word_index is None: continue # leaving `effective_words` unchanged = shortening the sentence = expanding the window if c.sample and vocab_sample_ints[word_index] < random_int32(&c.next_random): From 9caf2176938c16134a2eae6d920796942ee22621 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 16 Jul 2020 14:02:27 -0700 Subject: [PATCH 58/60] add default option to get_index; avoid exception in has_index_for --- gensim/models/keyedvectors.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 5a1684f290..21ad5e1883 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -337,15 +337,18 @@ def __getitem__(self, key_or_keys): return vstack([self.get_vector(key) for key in key_or_keys]) - def get_index(self, key): + def get_index(self, key, default=None): """Return the integer index (slot/position) where the given key's vector is stored in the backing vectors array. """ - if key in self.key_to_index: - return self.key_to_index[key] - elif isinstance(key, (int, np.integer)) and key < len(self.index_to_key): + val = self.key_to_index.get(key, -1) + if val >= 0: + return val + elif isinstance(key, (int, np.integer)) and key < len(self.index_to_key) and key >= 0: return key + elif default is not None: + return default else: raise KeyError("Key '%s' not present" % key) @@ -491,10 +494,7 @@ def has_index_for(self, key): more-specific check. """ - try: - return self.get_index(key) >= 0 - except KeyError: - return False + return self.get_index(key, -1) >= 0 def __contains__(self, key): return self.has_index_for(key) From 14dd9f51b169db025641ef5a9041d42f37b65a95 Mon Sep 17 00:00:00 2001 From: Gordon Mohr Date: Thu, 16 Jul 2020 14:57:34 -0700 Subject: [PATCH 59/60] chained range check MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Radim Řehůřek --- gensim/models/keyedvectors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gensim/models/keyedvectors.py b/gensim/models/keyedvectors.py index 21ad5e1883..7c386ac038 100644 --- a/gensim/models/keyedvectors.py +++ b/gensim/models/keyedvectors.py @@ -345,7 +345,7 @@ def get_index(self, key, default=None): val = self.key_to_index.get(key, -1) if val >= 0: return val - elif isinstance(key, (int, np.integer)) and key < len(self.index_to_key) and key >= 0: + elif isinstance(key, (int, np.integer)) and 0 <= key < len(self.index_to_key): return key elif default is not None: return default From 0d2679af1497365e35bdb0b06bba5959d6a6bf78 Mon Sep 17 00:00:00 2001 From: Michael Penkov Date: Sun, 19 Jul 2020 22:13:16 +0900 Subject: [PATCH 60/60] Update CHANGELOG.md --- CHANGELOG.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6828e3ac89..56eef755ca 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,8 +3,11 @@ Changes ## Unreleased +This release contains a major refactoring. + ### :+1: Improvements +* KeyedVectors & X2Vec API streamlining, consistency (PR [#2698](https://github.com/RaRe-Technologies/gensim/pull/2698), __[@gojomo](https://github.com/gojomo)__) * No more wheels for x32 platforms (if you need x32 binaries, please build them yourself). (__[menshikh-iv](https://github.com/menshikh-iv)__, [#6](https://github.com/RaRe-Technologies/gensim-wheels/pull/6)) * Speed up random number generation in word2vec model (PR [#2864](https://github.com/RaRe-Technologies/gensim/pull/2864), __[@zygm0nt](https://github.com/zygm0nt)__)