LikelihoodComposeQuantifier#

class mlquantify.compose.LikelihoodComposeQuantifier(representation=None, estimator=None, solver='slsqp', aggregative=True, tau_0=0.0, tau_1=0.0, random_state=None)[source]#

Compose quantifier based on mixture negative log-likelihood.

Minimizes the (optionally regularized) mixture negative log-likelihood to estimate class prevalences. When a nested representation is provided, per-class likelihoods are obtained from that representation; otherwise they are derived from the test posteriors and training priors.

This class is the backbone of the KDEy quantifier family.

Parameters:

representationBaseRepresentation or None, default=None: Optional representation (e.g. KDERepresentation) that exposes class_likelihoods. When None, the estimator’s posterior outputs are used directly.
estimatorclassifier or None, default=None: Base classifier. Required when aggregative=True.
solverstr, default=’slsqp’: Prevalence solver passed to minimize_prevalence.
aggregativebool, default=True: If True, the estimator is used to obtain predictions before computing the representation.
tau_0float, default=0.0: First-order ordinal smoothness regularization weight.
tau_1float, default=0.0: Second-order ordinal smoothness regularization weight.
random_stateint, RandomState instance, or None, default=None: Random seed for the SLSQP starting point.

Attributes:

classes_ndarray of shape (n_classes,): Class labels seen during fit.
train_priors_ndarray of shape (n_classes,): Empirical class proportions in the training data.
best_distance_float or None: Objective value at the optimum after the last predict call.

aggregate(test_representation, train_representation=None, train_labels=None, classes=None)[source]#

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')[source]#

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()[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 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[source]#: Save the quantifier instance to a file.

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.