KDEyQuantifier#

class mlquantify.matching.KDEyQuantifier(estimator=None, bandwidth=0.1, kernel='gaussian', solver='slsqp', cv=None, stratified=True, shuffle=False, random_state=None)[source]#

Abstract base class for KDE-based density matching quantifiers.

Fits kernel density estimates (KDEs) over classifier posterior probabilities for each class and estimates test prevalence by finding the mixture of class densities that best matches the test density.

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. See sklearn.neighbors.KernelDensity.

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._density import KDEyQuantifier
>>> from mlquantify.base_aggregative import SoftPredictionMixin, AggregativeMixin
>>> from mlquantify.matching._base import BaseMatchingQuantifier
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> import numpy as np
>>> class MyKDEy(KDEyQuantifier):
...     def _precompute_density_terms(self, train_scores=None,
...                                   train_labels=None, train_representation=None):
...         pass  # no precomputation needed
...     def _solve_prevalence(self, test_representation, train_representations):
...         alpha = np.mean(test_representation)
...         return np.array([1 - alpha, alpha]), None
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> MyKDEy(estimator=LogisticRegression()).fit(X, y).predict(X)
{0: 0.49, 1: 0.51}
get_distance(dist_train, dist_test, distance='hellinger')[source]#

Compute a distance between two normalized representations.

get_metadata_routing()[source]#

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)[source]#

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[source]#

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 = '$UNCHANGED$') → KDEyQuantifier[source]#

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)[source]#

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.