Frank McQuillan created MADLIB-1351:
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Summary: Add stopping criteria on perplexity to LDA
Key: MADLIB-1351
URL: https://issues.apache.org/jira/browse/MADLIB-1351
Project: Apache MADlib
Issue Type: New Feature
Components: Module: Parallel Latent Dirichlet Allocation
Reporter: Frank McQuillan
Fix For: v2.0
In LDA
http://madlib.apache.org/docs/latest/group__grp__lda.html
make stopping criteria on perplexity rather than just number of iterations.
Suggested approach is to do what scikit-learn does
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
evaluate_every : int, optional (default=0)
How often to evaluate perplexity. Only used in fit method. set it to 0 or
negative number to not evalute perplexity in training at all. Evaluating
perplexity can help you check convergence in training process, but it will also
increase total training time. Evaluating perplexity in every iteration might
increase training time up to two-fold.
perp_tol : float, optional (default=1e-1)
Perplexity tolerance in batch learning. Only used when evaluate_every is
greater than 0.
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