CDE

class mlquantify.likelihood.CDE(estimator=None, tol=0.0001, max_iter=100, init_cfp=1.0, strategy='ovr', n_jobs=None)

CDE-Iterate quantifier.

Targets prior probability shift. Estimates binary prevalence by iteratively adjusting the decision threshold using class-cost ratios derived from the training priors and the current prevalence estimate, re-counting at the new threshold until the predicted positive proportion stabilises. Binary-only; multiclass via one-vs-rest.

Parameters:

estimatorestimator, optional

A probabilistic classifier with fit and predict_proba methods.

tolfloat, default=1e-4

Convergence threshold on the positive prevalence change between iterations.

max_iterint, default=100

Maximum number of iterations.

init_cfpfloat, default=1.0

Initial cost of false positives; sets the starting cost ratio that the iteration then refines.

strategy{‘ovr’, ‘ovo’}, default=’ovr’

Multiclass decomposition strategy.

• 'ovr' : one-vs-rest, one binary quantifier per class.

• 'ovo' : one-vs-one, one binary quantifier per class pair.

n_jobsint or None, default=None

Number of parallel jobs for multiclass decomposition.

Attributes:

estimator_estimator

The fitted underlying classifier.

classes_ndarray of shape (n_classes,)

Class labels seen during fit.

priors_ndarray of shape (n_classes,)

Training class prevalences.

See also

EMQ

Expectation-Maximization likelihood quantifier.

ACC

Single-step adjusted count.

Notes

CDE couples threshold adjustment with cost-sensitive re-counting; it behaves like an iterative cousin of the ThresholdAdjustment family rather than a likelihood maximiser, despite living in this module.

References

References

[1]

Barranquero, J., Díez, J., & del Coz, J. J. (2015). Quantification-Oriented Learning Based on Reliable Classifiers. Pattern Recognition, 48(2), 591–604.

Examples

>>> from mlquantify.likelihood import CDE
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = CDE(estimator=LogisticRegression()).fit(X, y)
>>> q.predict(X)
{0: ..., 1: ...}
>>> # call aggregate with pre-computed posteriors
>>> proba = q.estimator_.predict_proba(X)
>>> q.aggregate(proba, proba, y)
{0: ..., 1: ...}

aggregate(predictions, y_train, classes=None)

Aggregate posteriors into prevalences using MoSS score simulation.

Searches over merging_factors to find the synthetic score distribution (generated by MoSS) whose histogram is closest to the test score distribution, then passes that synthetic set as the training reference to the base quantifier’s aggregate.

Parameters:

predictionsndarray of shape (n_samples, n_classes)

Posterior probabilities of the test instances.

y_trainndarray of shape (n_train_samples,)

Training class labels used to resolve class ordering.

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

Class labels the output must report, in order. When None they are inferred from y_train.

Returns:

prevalencesdict or ndarray of shape (n_classes,)

Estimated class prevalences.

Examples

>>> from mlquantify.meta import QuaDapt
>>> from mlquantify.matching import DyS
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = QuaDapt(DyS(LogisticRegression())).fit(X, y)
>>> proba = LogisticRegression().fit(X, y).predict_proba(X)
>>> q.aggregate(proba, y)
{0: ..., 1: ...}

fit(X, y)

Fit the base classifier of the wrapped quantifier.

Only the underlying estimator is trained here; the full aggregation is deferred to aggregate so that the MoSS-based correction can be applied at prediction time.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training feature matrix.

yarray-like of shape (n_samples,)

Training class labels.

Returns:

selfQuaDapt

The fitted quantifier.

Raises:

ValueError

If the wrapped quantifier does not use soft (probabilistic) predictions.

Examples

>>> from mlquantify.meta import QuaDapt
>>> from mlquantify.matching import DyS
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = QuaDapt(DyS(LogisticRegression())).fit(X, y)

fit_predict(X, y, X_test)

Fit and predict class prevalences without storing models.

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 using the MoSS adaptive correction.

Generates posterior probabilities for X with the fitted classifier and delegates to aggregate, which selects the best MoSS merging factor and calls the base quantifier’s aggregate.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test feature matrix.

Returns:

prevalencesdict or ndarray of shape (n_classes,)

Estimated class prevalences.

Examples

>>> from mlquantify.meta import QuaDapt
>>> from mlquantify.matching import DyS
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = QuaDapt(DyS(LogisticRegression())).fit(X, y)
>>> q.predict(X)
{0: ..., 1: ...}

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

Save the quantifier instance to a file.

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.