Add Categorical Naive Bayes

[lowener]

This is a continuation of PR #1763, #4053, and #4079, to add Categorical Naive Bayes.
This is supposed to be merged after #4079.
Linking issue #1666.

[lowener]

Here is a comparison of cuML and SKLearn performance on Categorical NB.
This is done using a synthetic dataset generated by make_classification with 4 classes.
The GPU used is a RTX 8000, and the CPU is i9-10920X @ 3.50GHz

[cjnolet]

@lowener can you include the steps taken for the timings in your benchmark chart? I'm mostly interested in whether these timings are only for training or if they also include the likelihoods.

[cjnolet]

Your implementaiton looks great overall. This algorithm is immensely popular on sparse inputs / bigraphs, though, so we should strive to support sparse inputs and, as a result, assume a significantly large upper-bound on the number of features.

[lowener]

I added support for sparse inputs and removed the loops over n_features.

For the benchmark previously posted I was only timing the fit() operation. Now the timing also include the predict():

CategoricalNB().fit(X, Y).predict(X)

And we can see that the removal of the loop over n_features greatly boosted the performances.

[cjnolet]

The changes look really good and the benchmarks are super impressive. Just one little cleanup opportunity remains.

[codecov-commenter]

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[cjnolet]

LGTM

[cjnolet]

@gpucibot merge