KDEyHD

class mlquantify.neighbors.KDEyHD(learner=None, bandwidth=0.1, kernel='gaussian', montecarlo_trials=1000, random_state=None)

KDEy Hellinger Distance Minimization quantifier.

Estimates class prevalences by minimizing the Hellinger distance ( HD ) between the KDE mixture of class-conditional densities and the KDE of test data, estimated via Monte Carlo sampling and importance weighting.

This stochastic approximation enables practical optimization of complex divergence measures otherwise lacking closed-form expressions for Gaussian Mixture Models.

Parameters:

montecarlo_trialsint

Number of Monte Carlo samples used in approximation.

random_stateint or None

Seed or random state for reproducibility.

References

Builds on f-divergence Monte Carlo approximations for KDE mixtures as detailed by Moreo et al. (2023) and importance sampling techniques.

best_distance(predictions, train_predictions, train_y_values)

Retorna a melhor distância encontrada durante o ajuste.

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.

save_quantifier(path: str | None = None) → None

Save the quantifier instance to a file.

set_fit_request(*, learner_fitted: bool | None | str = '$UNCHANGED$') → KDEyHD

Configure whether metadata should be requested to be passed to the fit 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 fit 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 fit.

• 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:

learner_fittedstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for learner_fitted parameter in fit.

Returns:

selfobject

The updated object.

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.