wtte-rnn
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ragulpr
WTTE-RNN a framework for churn and time to event prediction
Reverts ragulpr/wtte-rnn#62
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 *...
Hello! I am implementing WTTE RNN on self-collected data from a company, where I have a censorship rate around 50 %. There are 30 000 sequences with 500 timesteps. The...
What's the biggest dataset you've used with WTTE-RNN? I'm having consistent issues with this package whenever I try using functions in `wtte.transforms`. I am using this in a Jupyter notebook,...
Hi, I found there is some problem with data preprocessing functions. The problem is when we want to get result from our model for sequences and its id, when we...
Hello! Awesome work here! I was wandering how to derive model's c-index with this package. Would you please advise? Thanks, Ed
In the lecture/blog all the events are described as a single point in time. How can i go about modelling an event which has a duration? The only thing i...
The process I'm trying to model using WTTE-RNN has two "typical" churn times, though customers can churn at any other time (they're simply a-priori more likely). This means that, at...
I have tried to solve the problem with nan loss and I found this trick to be helpful: adding the epsilon constant to the argument of np.log: loglikelihoods = u...
What could be the reason for the Invalid Loss error to be present in GPU training and not in the CPU training? I've successfully trained the WTTE-RNN algorithm on a...
