In here is some code I used for plot_top_losses on Tabular models. You should be able to refactor this for your validation dataframe. Why are you trying to do this though? To me that seems like a lot of extra work
list_x = list(data_train_ds.x)
list_y = list(data_train_ds.y)
This directly converts Labellist and collablists into normal python list. Later you can convert them easily into numpy arrays or data-frames.
I ended doing the following to generate a list for later processing in a dataframe [str(i).split(';') for i in list(Kd_learn.data.valid_ds.x)]
I was playing with collaborative filtering. It turns out, if some of the items in the validation set were not seen in the training set, it will be labeled as #na#. As a first step, I need to ignore these predictions. I remember this was mentioned in the class, although don’t remember how to deal with it properly. I also need to get more familiar with dataloader in order to understand @muellerzr’s codes better.