Training a covariate model

Another example with 20 news dataset. This involves building a model using the labels as co-variates and provides a simple test of the functionality. Co-variate models, in general, should use co-variates that are somewhat orthogonal to the latent topics.

from tmnt.estimator import BowEstimator, CovariateBowEstimator
import numpy as np
import gluonnlp as nlp
import os
from sklearn.datasets import fetch_20newsgroups
from tmnt.preprocess.vectorizer import TMNTVectorizer
from tmnt.inference import BowVAEInferencer
from tmnt.distribution import LogisticGaussianDistribution, HyperSphericalDistribution

n_samples = 2000
n_features = 1000

data, y = fetch_20newsgroups(shuffle=True, random_state=1,
                             remove=('headers', 'footers', 'quotes'),
                             return_X_y=True)
data_samples = data[:n_samples]
tf_vectorizer = TMNTVectorizer(vocab_size=1000)
X, _ = tf_vectorizer.fit_transform(data_samples)

Example TODO …