There is no .process() for a DataLoader generated from your âoriginalâ dl used in training (as in a dl created from calling dls.test_dl). You can do that for your training dl though. Even after calling .process() and then creating a new .test_dl using my test data, the dataset is still unencoded (which makes sense, but I just wanted to mention that).
fastai2 version: 0.0.17
fastcore version: 0.1.17
Hi!
Do any of these notebooks contain an example of regression with multiple dependent variables? I am not sure exactly how to proceed.
Thanks!
No, Iâm afraid they donât out of the box. TabularPandas doesnât like that very much. My best recommendation would be using a NumPy DataLoader for tabular instead and working with it. See my article here @vrodriguezf https://muellerzr.github.io/fastblog/2020/04/22/TabularNumpy.html
Thank you, Iâll have a look! However, I am a bit confused. I just created a TabularDataLoaders with a list of two variables as y_names, and y_block=RegressionBlock(), and things seem to be working with MSE as loss function.
I did not even need to adjuts the n_out argument of RegressionBlock, which I was thinking at the beginning that could be used to define the number of output activations that you wanted in the regression.
You found the one exception 
if itâs two regressions it will work. However classification with regression or two classifications it will not
Oh, I understand! how lucky I am 
One extra question: I am not sure if I remember correctly, but: did fastai1 provide a way to automatically log transform the dependent variables? Is that provided as a ready-to-use Procs in fastai2?
EDIT: Sorry I just saw that Jeremy does it manually in one of the fastai2 lessons
Correct. V1 did, v2 doesnât
Hi again!
Is there an equivalent of plot_top_losses for a tabular learner?
Thanks!
Should work out of the box on any Interpretation object (ClassificationInterpretation or otherwise)
strange, I manage to get plot_confusion_matrix working, but not plot_top_losses(k=10). It does not raise any error, it just doesnât show up anything
Hmmm. Iâll take a look at it on my end shortly
In the meanwhile I am using the show method from TabularPandas, but instead of using the whole validation dataset, I manually subset it with the indices given by Interpretation.top_losses:
def show_top_losses(tab_learner, k=None, largest=True):
interp = Interpretation.from_learner(tab_learner)
top_losses = interp.top_losses(k, largest)
to_top_losses = tab_learner.dls.valid.dataset.iloc[top_losses.indices]
to_top_losses.show()
return to_top_losses
EDIT: This workaround just shows the rows associated to the top losses, but not the losses neither the predictions
@vrodriguezf the issue comes with _pre_show_batch. Itâs not returning the y's or the outs as it should be. Iâll file a bug report (As @sgugger may not be able to get to this for a bit). In the meantime nice workaround!
@vrodriguezf Re: GitHub issue: currently not implemented, Sylvain will look at it when he has time (and isnât doing the edits )
Good to know! thank you for raising the issue
Basic question about intepretability: Can fastshap be used for a regression learner? Should I use the premutation importance class instead?
You should be able to use it for regression IIRC (it was a collaboration project and I believe we tackled it). Otherwise, yes permutation importance would be good to use too always I canât recall if I had it set up specifically for classification or not, but it should be straightforward to adjust for regression too (just adjust what metric/loss function itâs using to generate itâs differences and possibly change how it uses them. IE MSE should favor a smaller number vs hinder such as accuracy)
Out of the box, calling ShapInterpretation with a regression learner raises an error due to the attribute vocab is not there :S
Iâll look at it when I have time, thanks!