Hands-On Machine Learning for Algorithmic Trading

How to implement LDA using sklearn

Using the BBC data as before, we use sklearn.decomposition.LatentDirichletAllocation to train an LDA model with five topics (see the sklearn documentation for detail on parameters, and the notebook lda_with_sklearn for implementation details):

lda = LatentDirichletAllocation(n_components=5,                                     n_jobs=-1,                                     max_iter=500,                                    learning_method='batch',                                     evaluate_every=5,                                    verbose=1,                                     random_state=42)ldat.fit(train_dtm)LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,             evaluate_every=5, learning_decay=0.7, learning_method='batch',             learning_offset=10.0, max_doc_update_iter=100, max_iter=500,             mean_change_tol=0.001, n_components=5, n_jobs=-1,             n_topics=None, perp_tol=0.1, random_state=42, topic_word_prior=None, ...