Pipelining and cross-validation

Integration with sklearn pipelines

Wrapped estimators can be used in sklearn pipelines without any additional overhead:

>>> from sklearn_xarray import wrap, Target
>>> from sklearn_xarray.datasets import load_digits_dataarray
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.decomposition import PCA
>>> from sklearn.linear_model.logistic import LogisticRegression
>>>
>>> X = load_digits_dataarray()
>>> y = Target(coord='digit')(X)
>>>
>>> pipeline = Pipeline([
...     ('pca', wrap(PCA(n_components=50), reshapes='feature')),
...     ('cls', wrap(LogisticRegression(), reshapes='feature'))
... ])
>>>
>>> pipeline.fit(X, y)  
Pipeline(...)
>>> pipeline.score(X, y)
1.0

Cross-validated grid search

Note

This feature is currently only available for DataArrays.

The module sklearn_xarray.model_selection contains the CrossValidatorWrapper class that wraps a cross-validator instance from sklearn.model_selection. With such a wrapped cross-validator, it is possible to use xarray data types with a GridSearchCV estimator:

>>> from sklearn_xarray.model_selection import CrossValidatorWrapper
>>> from sklearn.model_selection import GridSearchCV, KFold
>>>
>>> cv = CrossValidatorWrapper(KFold())
>>> pipeline = Pipeline([
...     ('pca', wrap(PCA(), reshapes='feature')),
...     ('cls', wrap(LogisticRegression(), reshapes='feature'))
... ])
>>>
>>> gridsearch = GridSearchCV(
...     pipeline, cv=cv, param_grid={'pca__n_components': [20, 40, 60]}
... )
>>>
>>> gridsearch.fit(X, y) 
GridSearchCV(...)
>>> gridsearch.best_params_
{'pca__n_components': 20}
>>> gridsearch.best_score_
0.9182110801609408