Provide wrappers for popular ML libraries

It’d be convenient to provide support for use of Keras or PyTorch models in model selection. There are two issues:

1. Keras/PyTorch models don’t conform to the Scikit-learn API.
2. Keras models are not pickle-able.

I’m imaging this interface:

from torchvision.models import resnet18
import torch.optim as optim
from dask_ml.wrappers import PyTorchClassifier

pytorch_model = resnet18()
sklearn_model = SkorchClassifier(
    model=pytorch_model,
    model__alpha=1e-2,  # if resnet18 had a kwarg `alpha`
    optimizer=optim.SGD,
    optimizer__lr=0.1,
)

Related issues/PRs Same complaint in dask/distributed: https://github.com/dask/distributed/issues/3873

[stsievert]

How difficult would it be to implement pickling … for Keras as well?

SciKeras has an implementation at scikeras/wrappers.py#L87. There’s currently an open PR to merge this into Tensorflow/Keras master: https://github.com/tensorflow/tensorflow/pull/39609

Does that answer your question?

[adriangb]

If you’re looking for a way to make Keras models conform to the scikit-learn API, check out SciKeras (full disclosure: I’m the author)