Hands-On Machine Learning for Algorithmic Trading

Create input data

The gensim.models.doc2vec class processes documents in the TaggedDocument format that contains the tokenized documents alongside a unique tag that permits accessing the document vectors after training:

sentences = []for i, (_, text) in enumerate(sample.values):    sentences.append(TaggedDocument(words=text.split(), tags=[i]))

The training interface works similar to word2vec with additional parameters to specify the Doc2vec algorithm:

model = Doc2vec(documents=sentences,                dm=1,          # algorithm: use distributed memory                dm_concat=0,   # 1: concat, not sum/avg context vectors                dbow_words=0,  # 1: train word vectors, 0: only doc                                     vectors                alpha=0.025,   # initial learning rate                size=300,                window=5,                min_count=10,                epochs=5,                negative=5)model.save('test.model') ...