FrozenEstimator (original) (raw)
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 callingfit 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
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.predictarray(...)
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:
selfobject
Returns the instance itself.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
get_params(deep=True)[source]#
Get parameters for this estimator.
Returns a {"estimator": estimator} dict. The parameters of the inner estimator are not included.
Parameters:
deepbool, default=True
Ignored.
Returns:
paramsdict
Parameter names mapped to their values.
Set the parameters of this estimator.
The only valid key here is estimator. You cannot set the parameters of the inner estimator.
Parameters:
**kwargsdict
Estimator parameters.
Returns:
selfFrozenEstimator
This estimator.