fishy.models.evolutionary.eda

Estimation of Distribution Algorithm (EDA) for feature weighting.

Classes

class fishy.models.evolutionary.eda.EDA(generations: int = 10, population_size: int = 100, select_ratio: float = 0.2, elitism: float = 0.1, random_state: int = 42)

Bases: BaseEstimator, ClassifierMixin

Scikit-learn compatible wrapper for Estimation of Distribution Algorithm.

__init__(generations: int = 10, population_size: int = 100, select_ratio: float = 0.2, elitism: float = 0.1, random_state: int = 42)

fit(X: ndarray, y: ndarray)

predict(X: ndarray) → ndarray

predict_proba(X: ndarray) → ndarray

Returns probability estimates for LIME support.

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

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:

self – The updated object.

Return type:

object

transform(X: ndarray) → ndarray

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