KDEyML

class mlquantify.neighbors.KDEyML(learner=None, bandwidth: float = 0.1, kernel: str = 'gaussian')

KDEy Maximum Likelihood quantifier.

Models class-conditional densities of posterior probabilities via Kernel Density Estimation (KDE) and estimates class prevalences by maximizing the likelihood of test data under a mixture model of these KDEs.

The mixture weights correspond to class prevalences, optimized under the simplex constraint. The optimization minimizes the negative log-likelihood of the mixture density evaluated at test posteriors.

This approach generalizes EM-based quantification methods by using KDE instead of discrete histograms, allowing smooth multivariate density estimation over the probability simplex.

References

The method is based on ideas presented by Moreo et al. (2023), extending KDE-based approaches for distribution matching and maximum likelihood estimation.

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 = '$UNCHANGED$') → KDEyML

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.