nemos.basis._basis.AdditiveBasis.to_transformer

AdditiveBasis.to_transformer()

Turn the Basis into a TransformerBasis for use with scikit-learn.

Return type:

TransformerBasis

Examples

Jointly cross-validating basis and GLM parameters with scikit-learn.

>>> import nemos as nmo
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.model_selection import GridSearchCV
>>> # load some data
>>> X, y = np.random.normal(size=(30, 1)), np.random.poisson(size=30)
>>> basis = nmo.basis.RaisedCosineLinearEval(10).set_input_shape(1).to_transformer()
>>> glm = nmo.glm.GLM(regularizer="Ridge", regularizer_strength=1.)
>>> pipeline = Pipeline([("basis", basis), ("glm", glm)])
>>> param_grid = dict(
...     glm__regularizer_strength=(0.1, 0.01, 0.001, 1e-6),
...     basis__n_basis_funcs=(3, 5, 10, 20, 100),
... )
>>> gridsearch = GridSearchCV(
...     pipeline,
...     param_grid=param_grid,
...     cv=5,
... )
>>> gridsearch = gridsearch.fit(X, y)