w2v.py

from gensim.models import word2vec
from os.path import join, exists, split
import os
import numpy as np

def train_word2vec(sentence_matrix, vocabulary_inv,
                   num_features=300, min_word_count=1, context=10):
    """
    Trains, saves, loads Word2Vec model
    Returns initial weights for embedding layer.
   
    inputs:
    sentence_matrix # int matrix: num_sentences x max_sentence_len
    vocabulary_inv  # dict {str:int}
    num_features    # Word vector dimensionality                      
    min_word_count  # Minimum word count                        
    context         # Context window size 
    """
    model_dir = 'word2vec_models'
    model_name = "{:d}features_{:d}minwords_{:d}context".format(num_features, min_word_count, context)
    model_name = join(model_dir, model_name)
    if exists(model_name):
        embedding_model = word2vec.Word2Vec.load(model_name)
        print('Loading existing Word2Vec model \'%s\'' % split(model_name)[-1])
    else:
        # Set values for various parameters
        num_workers = 2       # Number of threads to run in parallel
        downsampling = 1e-3   # Downsample setting for frequent words
        
        # Initialize and train the model
        print("Training Word2Vec model...")
        sentences = [[vocabulary_inv[w] for w in s] for s in sentence_matrix]
        embedding_model = word2vec.Word2Vec(sentences, workers=num_workers, \
                            size=num_features, min_count = min_word_count, \
                            window = context, sample = downsampling)
        
        # If we don't plan to train the model any further, calling 
        # init_sims will make the model much more memory-efficient.
        embedding_model.init_sims(replace=True)
        
        # Saving the model for later use. You can load it later using Word2Vec.load()
        if not exists(model_dir):
            os.mkdir(model_dir)
        print('Saving Word2Vec model \'%s\'' % split(model_name)[-1])
        embedding_model.save(model_name)
    
    #  add unknown words
    embedding_weights = [np.array([embedding_model[w] if w in embedding_model\
                                                        else np.random.uniform(-0.25,0.25,embedding_model.vector_size)\
                                                        for w in vocabulary_inv])]
    return embedding_weights

if __name__=='__main__':
    import data_helpers
    print("Loading data...")
    x, _, _, vocabulary_inv = data_helpers.load_data()
    w = train_word2vec(x, vocabulary_inv)