sklearn-xarray
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phausamann
Metadata-aware machine learning.
It should be possible to pass a callable as `reduce_index` which takes the number of samples after transformation as an input and returns an index of this length.
First of all: great software combining sklearn and xarray @phausamann My example here is inspired from #52, an example which helped me a lot. Input data: forecast with dimension T...
Make sure you have: - [ ] added the necessary updates to `doc/content/whatsnew.rst` - [ ] added documentation for new features - [ ] run bumpversion and reset conda build...
This is the initial version of the wrapper to convert gridsearchCV results per the thread in the discussion. Codewise, I use a multindexed pandas to create the Dataset. Perhaps there...
The `reshapes` parameter is confusing and in reality only necessary if the number of dimensions changes. Proposal: 1. Check if number of dimensions has changed, if yes check for reshapes...
`attrs` and `name` are dropped e.g. in [`_call_fitted`](https://github.com/phausamann/sklearn-xarray/blob/0a8e61222a89e02665f444233e2bb2eb2bef7184/sklearn_xarray/common/base.py#L199).
I think that sklearn-x could be a significant contribution to folks working on satellite remote sensing. I am REALLY excited by it! I am however have a hard time figure...
These methods do not work for pipelines/estimators that change the number of samples, because of the `check_consistent_length` check.
A lot of the tests aren't really "unit" tests, but rather integration tests for the wrappers.
This is going to be a serious issue as of sklearn version 0.20 because `clone` will raise an exception. The only workaround is to use `wrap` with `compat=True`. This seems...