GKDEyML

class mlquantify.matching.GKDEyML(estimator=None, bandwidth=0.1, kernel='gaussian', solver='slsqp', cv=None, stratified=True, shuffle=False, random_state=None)

Generalized KDEy Maximum Likelihood (GKDEyML) quantifier.

Multiclass extension of KDEy using the likelihood-composition framework. Fits KDE densities over classifier posterior probabilities for each class and estimates prevalences by maximising the mixture log-likelihood.

Parameters:

estimatorestimator, optional

A probabilistic classifier with fit and predict_proba methods.

bandwidthfloat, default=0.1

Bandwidth of the kernel density estimator.

kernelstr, default=’gaussian’

Kernel type for the KDE.

solverstr, default=’slsqp’

Optimization solver.

cvint or None, default=None

Cross-validation folds for computing training scores.

stratifiedbool, default=True

Whether to stratify CV splits.

shufflebool, default=False

Whether to shuffle data before splitting.

random_stateint or None, default=None

Random seed for reproducibility.

Attributes:

estimator_estimator

The fitted underlying classifier.

classes_ndarray of shape (n_classes,)

Class labels seen during fit.

References

References

[1]

Moreo, A., González, P., & del Coz, J. J. (2024). Kernel Density Estimation for Multiclass Quantification. Machine Learning, 113, 3075–3107.

Examples

>>> from mlquantify.matching import GKDEyML
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, n_classes=3, n_informative=5,
...                            n_redundant=0, random_state=42)
>>> q = GKDEyML(estimator=LogisticRegression()).fit(X, y)
>>> q.predict(X)
{0: 0.33, 1: 0.34, 2: 0.33}

aggregate(test_representation, train_representation=None, train_labels=None, classes=None)

Aggregate a pre-computed test representation into prevalences.

Allows calling the compose quantifier’s solver directly when the representation has already been computed externally, bypassing the estimator step.

Parameters:

test_representationarray-like of shape (representation_dim,)

Pre-computed test representation vector.

train_representationarray-like of shape (n_samples, representation_dim) or None, default=None

Training representation. If provided together with train_labels, the representation is re-fitted.

train_labelsarray-like of shape (n_samples,) or None, default=None

Training labels used to re-fit the representation when train_representation is also provided.

classesarray-like of shape (n_classes,) or None, default=None

Class labels to use. Inferred from train_labels when None.

Returns:

prevalencesdict or ndarray of shape (n_classes,)

Estimated class prevalences.

fit(X, y, estimator_fitted=False, sample_weight=None, cv_prediction='refit')

Fit the likelihood compose quantifier.

When representation is provided the fitting is delegated to the parent BaseComposeQuantifier. Otherwise, only the estimator is trained (the likelihood is evaluated directly from posteriors at prediction time).

Parameters:

Xarray-like of shape (n_samples, n_features)

Training feature matrix.

yarray-like of shape (n_samples,)

Training class labels.

estimator_fittedbool, default=False

Skip fitting the estimator if already fitted.

sample_weightarray-like of shape (n_samples,) or None, default=None

Per-sample weights.

cv_prediction{‘refit’, ‘ensemble’}, default=’refit’

Cross-validation strategy.

Returns:

selfLikelihoodComposeQuantifier

The fitted quantifier.

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.

predict(X)

Predict class prevalences by minimizing mixture NLL.

When representation is set, delegates to the parent class. Otherwise computes per-class likelihoods from the estimator’s posteriors and minimizes the (regularized) mixture NLL.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test feature matrix.

Returns:

prevalencesdict or ndarray of shape (n_classes,)

Estimated class prevalences.

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

Save the quantifier instance to a file.

set_fit_request(*, cv_prediction: bool | None | str = '$UNCHANGED$', estimator_fitted: bool | None | str = '$UNCHANGED$', sample_weight: bool | None | str = '$UNCHANGED$') → GKDEyML

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:

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

Metadata routing for cv_prediction parameter in fit.

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

Metadata routing for estimator_fitted parameter in fit.

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

Metadata routing for sample_weight 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.