Comments (8)
I think this can be related to a problem in the torch installation. Can you check that you can do torch::torch_tensor(1)
in that machine?
from tabnet.
Hi,
Its giving the same error msg when i do that. "The previous R session was abnormally terminated due to an unexpected crash.
from tabnet.
Hello @G3Jram ,
so here you encounter a torch::
installation issue, not a tabnet::
issue. Torch is the underlying deeplearning framework of Tabnet. I think it is worth for you to reoppen the issue in here: https://github.com/mlverse/torch/issues, if the problem remains after you check all the installation prerequisite in https://torch.mlverse.org/docs/articles/installation.html.
Hope it will helps you enjoy using Tabnet.
from tabnet.
Hi,
I have succeeded in implementing tabnet in python instead. Waiting for the torch error to get sorted on the R side..
However, after building the model if i want to look at the feature importances, im not too sure how to do it.
Tabnet doesnt need shap to understand the features right?..
I actually want to plot the features by importance.
Please help.
from tabnet.
Hello @G3Jram ,
the R implementation includes the tabnet_explain()
function with associated plot that will give you feature importance for each prediction as documented in https://mlverse.github.io/tabnet/articles/interpretation.html. This provides you the internal model feature importance, so you don't have to suffer the drawbacks of surrogate models like shap.
from tabnet.
we have a post on that in fact: https://blogs.rstudio.com/ai/posts/2021-02-11-tabnet/
from tabnet.
Hi,
Sorry if i wasnt clear. I am after the feature importance in python not R for now as i still face torch installation issues in R.
Any help in python would be good.
Thanks.
from tabnet.
You can find examples for the python API here: https://github.com/dreamquark-ai/tabnet/blob/develop/forest_example.ipynb
Also, I think for questions on the Python interface It's better if you ask in their repository: https://github.com/dreamquark-ai/tabnet
from tabnet.
Related Issues (20)
- how tabnet with perform in the p >> n situation? HOT 3
- Problem with training HOT 2
- Using tabnet_config() inside fit_resamples HOT 2
- Trouble with learning when using device = "auto" configuration HOT 1
- Trouble with learning when tune_grid() with CPU parallel processing HOT 3
- `tune_grid()` and `multi_predict()` HOT 1
- move parsnip to depends HOT 1
- tabnet() needs an engine value HOT 1
- multiclass classification, why such a bad result? HOT 4
- Error with parameter tuning HOT 3
- Add a vignette about network design and data prep best practices
- hyperparameter tuning questions HOT 2
- Make feature importance calc optional when fitting tabnet HOT 2
- Allow sparse matrix input to tabnet HOT 1
- Error when loading RDS workflow. HOT 2
- Error when finalizing workflow
- add a {butcher} method for tabnet models
- Supplying validation set manually HOT 1
- Error sending tune parameters to tabnet via parsnip/tune HOT 6
- Exporting tab-network.R torch modules HOT 1
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