FrozenEstimator#

class sklearn.frozen.FrozenEstimator(estimator)[source]#

Estimator that wraps a fitted estimator to prevent re-fitting.

This meta-estimator takes an estimator and freezes it, in the sense that calling fit on it has no effect. fit_predict and fit_transform are also disabled. All other methods are delegated to the original estimator and original estimator’s attributes are accessible as well.

This is particularly useful when you have a fitted or a pre-trained model as a transformer in a pipeline, and you’d like pipeline.fit to have no effect on this step.

Parameters:

estimatorestimator: The estimator which is to be kept frozen.

See also

None: No similar entry in the scikit-learn documentation.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.frozen import FrozenEstimator
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_classification(random_state=0)
>>> clf = LogisticRegression(random_state=0).fit(X, y)
>>> frozen_clf = FrozenEstimator(clf)
>>> frozen_clf.fit(X, y)  # No-op
FrozenEstimator(estimator=LogisticRegression(random_state=0))
>>> frozen_clf.predict(X)  # Predictions from `clf.predict`
array(...)

fit(X, y, *args, **kwargs)[source]#

No-op.

As a frozen estimator, calling fit has no effect.

Parameters:

Xobject: Ignored.
yobject: Ignored.
*argstuple: Additional positional arguments. Ignored, but present for API compatibility with self.estimator.
**kwargsdict: Additional keyword arguments. Ignored, but present for API compatibility with self.estimator.

Returns: