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For easier usage, the package should replicate all sklearn estimators in their respective modules by decorating them with a generalized EstimatorWrapper.
Users could then use all sklearn estimators just by modifying their import statements, for example:
from sklearn_xarray.decomposition import PCA
which would yield an xarray-compatible PCA estimator.
The text was updated successfully, but these errors were encountered:
On second thought, class decorators seem like a bad idea, mostly because the resulting object is not pickleable. It makes more sense for each estimator to subclass the corresponding wrapper, like
class PCA(TransformerWrapper):
def __init__(self, **fit_params):
super(self, PCA).__init__(sklearn.decomposition.PCA, **fit_params)
and provide the full parameter list instead of **fit_params
For easier usage, the package should replicate all sklearn estimators in their respective modules by decorating them with a generalized
EstimatorWrapper
.Users could then use all sklearn estimators just by modifying their import statements, for example:
from sklearn_xarray.decomposition import PCA
which would yield an xarray-compatible
PCA
estimator.The text was updated successfully, but these errors were encountered: