ACC

class mlquantify.counting.ACC(estimator=None, strategy='ovr', n_jobs=None, cv=5, stratified=True, shuffle=False, random_state=None)

Adjusted Classify and Count (ACC) quantifier.

Targets prior probability shift. Runs CC and corrects the biased count using the classifier’s true- and false-positive rates, which are estimated by cross-validation and assumed invariant across the shift. The count, TPR and FPR are all derived from the classifier’s argmax hard prediction. This is a binary-only method; multiclass problems are handled with a one-vs-rest (OvR) strategy.

Parameters:

estimatorestimator, optional

A classifier with fit, predict_proba, and predict methods.

strategy{‘ovr’, ‘ovo’}, default=’ovr’

Multiclass decomposition strategy.

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

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

n_jobsint or None, default=None

Number of parallel jobs for multiclass decomposition.

cvint, default=5

Cross-validation folds used when estimator_fitted=False.

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.

See also

CC

Classify and Count quantifier.

TAC

Adjusted count at a fixed soft threshold.

GACC

Multiclass generalisation via constrained regression.

Notes

ACC corrects the linear bias of CC and is unbiased when the estimated TPR/FPR match the test set. When TPR - FPR is small (a weak classifier under heavy imbalance) the correction amplifies estimation error, trading bias for variance. The implementation follows the reference QoT formulation, deriving TPR, FPR and the count from argmax hard predictions rather than a soft-probability threshold.

References

References

[1]

Forman, G. (2005). Counting Positives Accurately Despite Inaccurate Classification. ECML, pp. 564–575.

[2]

Forman, G. (2008). Quantifying Counts and Costs via Classification. Data Mining and Knowledge Discovery, 17(2), 164–206.

Examples

>>> from mlquantify.counting import ACC
>>> from sklearn.linear_model import LogisticRegression
>>> import numpy as np
>>> X, y = np.random.randn(100, 5), np.random.randint(0, 2, 100)
>>> q = ACC(LogisticRegression()).fit(X, y)
>>> q.predict(X)
{0: ..., 1: ...}
>>> # aggregate() path with pre-computed hard predictions
>>> preds = q.estimator_.predict(X)
>>> q.aggregate(preds, q.train_predictions, q.y_train)
{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_fit_request(*, cv_prediction: bool | None | str = '$UNCHANGED$', estimator_fitted: bool | None | str = '$UNCHANGED$') → ACC

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.

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.