Hands-On Machine Learning for Algorithmic Trading

How to implement pLSA using sklearn

pLSA is equivalent to non-negative matrix factorization using a Kullback-Leibler Divergence objective (see references on GitHub https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-Algorithmic-Trading). Hence, we can use the sklearn.decomposition.NM class to implement this model, following the LSA example.

Using the same train-test split of the DTM produced by the TfidfVectorizer, we fit pLSA as follows:

nmf = NMF(n_components=n_components,random_state=42,solver='mu',beta_loss='kullback-leibler',max_iter=1000)nmf.fit(train_dtm)

We get a measure of the reconstruction error, which is a substitute for the explained variance measure from before:

nmf.reconstruction_err_316.2609400385988

Due to its ...