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slicetca.grid_search returned nan #1
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Hi, when I run the grid_search with a mask I don't get nans in loss_grid, so I'd need a bit more information to reproduce the bug:
The pipeline schematized in Fig. 3. is roughly what is done in the notebook. |
Thank you for the reply.
One thing I notice is that after running "slicetca.grid_search" in my environment, "reconstructed_noisy_tensor" is changed to the matrices containing only zero and "train_mask" and "test_mask" are changed to the matrix containing only False. (before running "slicetca.grid_search", the value of these matrices are normal.) Another thing is that the number of true and false in train_mask and test_mask is different between my environment and Colab environment (as shown below). Do you have any idea about the cause of these problem? The number of true in train_mask: 6024557 Colab: The number of true in train_mask: 6027016 |
I tried running the example notebook with Python 3.10.12 and torch 2.3.0 but I still can't reproduce the issue. Perhaps you could try to run this to check this is not an issue with the notebook:
I indeed get a non-nan loss_grid. The test_mask doesn't get modified. Note that to check the proportion of masked entries you can do Regarding the number of masked entries, I believe this is just a difference in the RNG seeds. |
When I run the given code once in my setup, loss_grid was nan. However when I run twice, I could get non-nan loss_grid. I'm sorry for the ambiguous comments. It's just case reports. |
Hi. Thank you for developing the great tool for analysis.
When I run the below code in sliceTCA_notebook_1.ipynb, the value of loss_grid was "nan" while If I deleted "mask_train" and "mask_test", loss_grid returned something. Do you know how to solve this problem? I did not modify any part except "sample_size".
Another requirement
Could you share an additional code which describe the flow of analysis in figure 3 of Pellegrino et al paper?
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