PS: obviously forcing conversion to numpy is not what we would want, rather
passing the underlying array of the DataArray.
Peter Hausamann schrieb am Mo., 4. Dez. 2017 um
17:25 Uhr:
> Thanks everyone for your feedback.
>
> The reason you're getting the error is because the first argument of
> Da
Thanks everyone for your feedback.
The reason you're getting the error is because the first argument of
DataArray.mean() is the named dimension 'dim' and not 'axis'. So calling
X.mean(axis=0) would probably solve the problem... but it might be easier
(and more robust) to fix this on my end by alwa
I haven't looked at the implementation of `sklearn_xarray.dataarray.wrap`
yet, but a simple test
on `dask_ml.preprocessing.StandardScaler` failed with the (probably
expected) `TypeError: 'int' object is not iterable`
when dask-ml attempts an `X.mean(0)`.
I'd be interested to hear what changes dask
Interesting project!
BTW, do you know about dask-ml [1]?
It might be interesting to think about generalizing the input validation of
fit and predict / transform as a private method of the BaseEstimator class
instead of directly calling into sklearn.utils.validation functions so has
to make it eas
