Hands-On Machine Learning for Algorithmic Trading

Model training

The gensim.models.Word2vec class implements the SG and CBOW architectures introduced previously. The Word2vec notebook contains additional implementation detail.

To facilitate memory-efficient text ingestion, the LineSentence class creates a generator from individual sentences contained in the provided text file:

sentence_path = Path('data', 'ngrams', f'ngrams_2.txt')sentences = LineSentence(sentence_path)

The Word2vec class offers the configuration options previously introduced:

model = Word2vec(sentences,                 sg=1,    # 1=skip-gram; otherwise CBOW                 hs=0,    # hier. softmax if 1, neg. sampling if 0                 size=300,      # Vector dimensionality                 window=3,      # Max dist. btw target and context word                 min_count=50,  # Ignore words with lower frequency                 negative ...