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:

ensemble (ModelLike) – sklearn compatible ensemble model. E.g RandomForest
algorithm – algorithm from deslib to make the model dynamic. Options:

__init__(ensemble, algorithm, ds_split=0.2, random_state=None, random_state_algorithm=None, **kwargs)¶

fit(X, y)¶

Fit the model.

Parameters:

X (ndarray | DataFrame | Series) – The data to fit the model on.
y (ndarray | DataFrame | Series) – The target data.

Returns:

The fitted model.

predict(X)¶

Predict using the model.

Parameters:: X (ndarray | DataFrame | Series) – The data to predict on.
Returns:: The predictions.

predict_proba(X)¶

Compute probabilities of possible outcomes for samples in X.

Parameters:: X (ndarray | DataFrame | Series) – The data to predict on.
Returns:: 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:

X (ndarray | DataFrame | Series) – The data to predict on.
y (ndarray | DataFrame | Series) – The true target values.
sample_weight (ndarray | DataFrame | Series | None, default: None) – Sample weights to use when computing the score (default None).

Returns:

The score.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: A MetadataRequest encapsulating routing information.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: 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:: **params (dict) – Estimator parameters.
Returns:: Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → DynamicSelection¶

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: The updated object.