LDA implementation #1132
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LDA implementation #1132
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## master #1132 +/- ##
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+ Coverage 90.27% 90.56% +0.29%
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Files 93 95 +2
Lines 7341 7518 +177
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+ Hits 6627 6809 +182
+ Misses 714 709 -5
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| def logistic_normal_approximation( | ||
| alpha: torch.Tensor, |
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can you add a comment/docstring about what's going on here?
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Also realized that the sigma here was the variance when I should be returning the std, don't think it actually changes a lot though
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Autoencoder Variational Bayes implementation of Latent Dirichlet Allocation as a PyroModule. Runs in the same magnitude of time as sklearn's implementation of LDA and achieves better perplexity on average. See benchmark here: https://colab.research.google.com/drive/1Iq_drlBTLadM8KJtZwIs96RdzG8MmEFA?authuser=1#scrollTo=lINSWBVshwvr.
Note: to have this work with reparametrization stably, I used a logistic normal to approximate the dirichlet distribution.