GPAC#

class mlquantify.adjust_counting.GPAC(learner=None)[source]#

Probabilistic Generalized Adjusted Count (GPAC) method.

This class implements the probabilistic extension of the Generalized Adjusted Count method as presented in Firat (2016) [1]. The GPAC method normalizes the confusion matrix by the estimated prevalences from posterior probabilities, enabling a probabilistic correction of class prevalences.

The normalization divides each column of the confusion matrix by the estimated prevalence of the corresponding class. If a class has zero estimated prevalence, the diagonal element for that class is set to 1 to maintain matrix validity.

GPAC extends the GAC approach by using soft probabilistic predictions (posterior probabilities) rather than crisp class labels, potentially improving quantification accuracy when posterior probabilities are well calibrated.

Parameters:
learnerestimator, optional

Base classifier with fit and predict_proba methods.

Attributes:
CMndarray of shape (n_classes, n_classes)

Normalized confusion matrix used for adjustment.

classes_ndarray

Array of class labels observed during training.

References

[1]

Firat, A. (2016). “Unified Framework for Quantification”, Proceedings of AAAI Conference on Artificial Intelligence, pp. 1-8.

Examples

>>> from sklearn.linear_model import LogisticRegression
>>> from mlquantify.adjust_counting import GPAC
>>> import numpy as np
>>> gpac = GPAC(learner=LogisticRegression())
>>> X = np.random.randn(50, 4)
>>> y = np.random.randint(0, 2, 50)
>>> gpac.fit(X, y)
>>> gpac.predict(X)
{0: 0.5, 1: 0.5}
aggregate(predictions, train_predictions, y_train_values)[source]#

Aggregate predictions and apply matrix- or rate-based bias correction.

fit(X, y, learner_fitted=False, cv=10, stratified=True, random_state=None, shuffle=True)[source]#

Fit the quantifier using the provided data and learner.

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.

predict(X)[source]#

Predict class prevalences for the given data.

save_quantifier(path: str | None = None) → None[source]#

Save the quantifier instance to a file.

set_fit_request(*, cv: bool | None | str = '$UNCHANGED$', learner_fitted: bool | None | str = '$UNCHANGED$', random_state: bool | None | str = '$UNCHANGED$', shuffle: bool | None | str = '$UNCHANGED$', stratified: bool | None | str = '$UNCHANGED$') → GPAC[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:
cvstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cv parameter in fit.

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

Metadata routing for learner_fitted parameter in fit.

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

Metadata routing for random_state parameter in fit.

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

Metadata routing for shuffle parameter in fit.

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

Metadata routing for stratified 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.