Loss Function - Not the PCF?

ragulpr / wtte-rnn

WTTE-RNN a framework for churn and time to event prediction

MIT License

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Loss Function - Not the PCF? #56

Open JonStevensWork opened 5 years ago

JonStevensWork commented 5 years ago

Hello, when looking at the likelihood function for the Weibull, I derived a different function than you do. Here is your function:

loglikelihoods = censored (K.log(shape) + shape K.log(x)) - K.pow(x, shape)

However when I do this, I end up with a K.log(scale) term as well. Can you explain how you arrived at your loglikelihood function? I can provide my working if necessary

michael-tsel commented 5 years ago

Totally agree, made a pull request

ragulpr commented 4 years ago

Hi there, thanks for contributing! I added some comments on PR https://github.com/ragulpr/wtte-rnn/pull/59

Otherwise I recommend reading

Proposition 2.26 and chapter 3.1.1 and 3.2 in particular shows some alternative forms of the loss function. http://publications.lib.chalmers.se/records/fulltext/253611/253611.pdf

Is there any qualitative difference to the current form? I'd be happy to see how you arrived at your definition