Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the What you really need to know section for the big picture.

julearn.models.dynamic.DynamicSelection#

class julearn.models.dynamic.DynamicSelection(ensemble, algorithm, ds_split=0.2, random_state=None, random_state_algorithm=None, **kwargs)#

Model to use dynamic selection algorithms from DESlib.

Parameters:
ensembleModelLike

sklearn compatible ensemble model. E.g RandomForest

algorithmstr

algorithm from deslib to make the model dynamic. Options:

  • METADES

  • SingleBest

  • StaticSelection

  • StackedClassifier

  • KNORAU

  • KNORAE

  • DESP

  • OLA

  • MCB

  • KNOP

ds_splitfloat, optional

How to split the training data. One split is used to train the ensemble model and the other to train the dynamic algorithm, by default .2 You can use any sklearn cv consistent cv splitter, but only with n_splits = 1.

random_stateint, optional

random state to get reproducible train test splits in case you use a float for ds_split (default is None).

random_state_algorithmint, optional

random state to get reproducible Deslib algorithm models (default is None).

**kwargsAny

Any additional parameters to pass to the deslib algorithm.

__init__(ensemble, algorithm, ds_split=0.2, random_state=None, random_state_algorithm=None, **kwargs)#
fit(X, y)#

Fit the model.

Parameters:
XDataLike

The data to fit the model on.

yDataLike

The target data.

Returns:
DynamicSelection

The fitted model.

predict(X)#

Predict using the model.

Parameters:
XDataLike

The data to predict on.

Returns:
DataLike

The predictions.

predict_proba(X)#

Compute probabilities of possible outcomes for samples in X.

Parameters:
XDataLike

The data to predict on.

Returns:
np.ndarray

Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

score(X, y, sample_weight=None)#

Score the model.

Parameters:
Xpd.DataFrame

The data to predict on.

yDataLike

The true target values.

sample_weightDataLike, optional

Sample weights to use when computing the score (default is None).

Returns:
float

The score.

get_metadata_routing()#

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)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight='$UNCHANGED$')#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.