rm Word2VecTrainables class

gensim/models/base_any2vec.py

@@ -83,8 +83,6 @@ def __init__(self, workers=3, vector_size=100, epochs=5, callbacks=(), batch_wor
         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)
@@ -814,7 +812,7 @@ def build_vocab(self, sentences=None, corpus_file=None, update=False, progress_p
         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.trainables.prepare_weights(self.hs, self.negative, self.wv, update=update, vocabulary=self)
+        self.prepare_weights(update=update, vocabulary=self)
 
     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.
@@ -861,8 +859,7 @@ def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=No
         # 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.trainables.prepare_weights(
-            self.hs, self.negative, self.wv, update=update, vocabulary=self)  # build tables & arrays
+        self.prepare_weights(update=update, vocabulary=self)  # 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.
@@ -885,9 +882,9 @@ def estimate_memory(self, vocab_size=None, report=None):
         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
+            report['syn1'] = vocab_size * self.layer1_size * dtype(REAL).itemsize
         if self.negative:
-            report['syn1neg'] = vocab_size * self.trainables.layer1_size * dtype(REAL).itemsize
+            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",
@@ -1004,8 +1001,8 @@ def _get_thread_working_mem(self):
             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)
+        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):
@@ -1078,7 +1075,7 @@ 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.trainables.layer1_size, self.sg,
+            self.workers, len(self.wv.vocab), self.layer1_size, self.sg,
             self.hs, self.sample, self.negative, self.window
         )
 
@@ -1122,10 +1119,10 @@ def load(cls, *args, **kwargs):
             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, 'vectors_lockf') and hasattr(model.wv, 'vectors'):
+            model.vectors_lockf = ones(len(model.wv.vectors), dtype=REAL)
         if not hasattr(model, 'random'):
-            model.random = random.RandomState(model.trainables.seed)
+            model.random = random.RandomState(model.seed)
         if not hasattr(model, 'train_count'):
             model.train_count = 0
             model.total_train_time = 0

gensim/models/deprecated/word2vec.py

@@ -169,7 +169,7 @@ 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,
+        'vector_size': vector_size,
         'alpha': old_model.alpha,
         'window': old_model.window,
         'min_count': old_model.min_count,
@@ -195,11 +195,11 @@ def load_old_word2vec(*args, **kwargs):
     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
+        new_model.syn1 = old_model.syn1
     if hasattr(old_model, 'syn1neg'):
-        new_model.trainables.syn1neg = old_model.syn1neg
+        new_model.syn1neg = old_model.syn1neg
     if hasattr(old_model, 'syn0_lockf'):
-        new_model.trainables.vectors_lockf = old_model.syn0_lockf
+        new_model.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

gensim/models/keyedvectors.py

@@ -220,6 +220,11 @@ 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)