awehenkel / umnn Goto Github PK

When running ToyExperiments.py, I run into an issue with the UMNNMAFFlow.invert function. As defined in UMNNMAFFlow.py it only takes arguments "z" and "context". However, the arguments given are z, 5, and "ParallelSimpler". Removing the second argument (5) makes the required plot, however, I am not sure what it is supposed to do.

I can send a pull request deleting the second argument.

Creating model...
Model created.
epoch: 0 - Train loss: 6.782076 - Test loss: 6.447329 - Elapsed time per epoch 0.064852 (seconds)
Traceback (most recent call last):
File "ToyExperiments.py", line 186, in
train_toy(toy, load=args.load, folder=args.folder)
File "ToyExperiments.py", line 163, in train_toy
summary_plots(x, x_test, folder, epoch, model, ll_tot, ll_test)
File "ToyExperiments.py", line 70, in summary_plots
x_pred = model.invert(z, 5, "ParallelSimpler")
TypeError: invert() takes from 2 to 3 positional arguments but 4 were given

Running python ToyExperiments.py does not seem to automatically download the UCI datasets for me.

I found the function force_lipschitz in the code, but I did not see where it was used. Is this constraint used or really necessary in the codebase?

Hello,
I would want to understand if with this approach is possible to apply monotonicity on more than 1 feature at a time from the input space.
Thank you.

Hello Antoine, I was just looking at your paper and noticed that the latest arXiv version still says "The source code to reproduce our experiments will be made available on Github at the end of the reviewing process". It might be nice to put the link to this (very nice) repo in the paper so that it's slightly easier to find.

Hello,

Could you please add a license, for example MIT, to make your code reusable?

Best,
Hadrien