Is there a function to generate a train set and test set from DataFrame？

[cloga]

I find sandbox>tool has cross_validate.py, and there are many functions from sklearn include split , are there any update?

[jseabold]

In the past, I've just edited the scikit-learn functions to return indices and done the slicing myself.

[jseabold]

Added the 0.6.0 milestone to this. It would be pretty easy to adapt all of the CV tools to be DataFrame aware.

[josef-pkt]

@cloga This part is asleep until we pick up on more cross-validation and bootstrap support.

My plan was to copy an updated version from scikit-learn, but this time in a separate module, and add a statsmodels module with the extra iterators or generators that we need. And based on the pandas issue, we would also need to add support for pandas dataframes.

Whenever I needed something similar recently it was faster to write a few lines, than to look for premade functions. Until now we haven't consolidated our usage of these iterators, and they are still spread out over several modules and scripts.

[Dimkoim]

Hello! I would like to work on this issue. Is anyone working right now on that?

[josef-pkt]

@Dimkoim AFAIK, Nobody is working on this.

I haven't worked on those since we added them, but ran into some related issues while working or thinking about other parts:

I just ran into a case where we use the LOOO iterator in outlier_influence. However, I would like to change the LOOO iterator to take an index for which observations to leave out instead of doing the full leave every observation, one at a time out.
That is, I only want to look at some LOOO cases instead of all, because most of them will be uninteresting (non-influential in my current application).

Aside 1: When we use the split iterators for the models, then we need to watch out for extra arrays/series that were used in the model.__init__. There is currently no generic support for subsampling those extra arrays, although it's easier for specific model where we know what extra arrays are used. #4741

Aside 2:
#4682 I think we need a more general splitter that preserves full rank of the design matrix, e.g. when there are categoricals.
#4680 some thoughts on a random resampler that also returns the out of sample index/mask.

[Dimkoim]

@josef-pkt Thanks for the reply! I was thinking for a more general split function, like the one that scikit has. What's your opinion?