CC#

class mlquantify.counting.CC(estimator=None, threshold=0.5)[source]#

Classify and Count (CC) quantifier.

Estimates class prevalences by classifying each test instance and counting the proportion assigned to each class. This is the simplest quantification baseline and tends to be biased when the class distribution differs between training and test data.

Parameters:
estimatorestimator, optional

A classifier with fit and predict methods. If None, call aggregate directly with pre-computed predictions.

thresholdfloat, default=0.5

Decision threshold applied to soft scores to produce hard labels. Must be in [0.0, 1.0].

Attributes:
estimator_estimator

The fitted underlying classifier.

classes_ndarray of shape (n_classes,)

Class labels seen during fit.

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 CC
>>> from sklearn.linear_model import LogisticRegression
>>> import numpy as np
>>> X, y = np.random.randn(100, 5), np.random.randint(0, 2, 100)
>>> q = CC(LogisticRegression()).fit(X, y)
>>> q.predict(X)
{0: 0.47, 1: 0.53}
>>> # call aggregate directly with pre-computed hard predictions
>>> preds = q.estimator_.predict(X)
>>> q.aggregate(preds, y_train=y)
{0: 0.47, 1: 0.53}
aggregate(predictions, y_train=None)[source]#

Aggregate predictions into class prevalence estimates.

Parameters:
predictionsndarray of shape (n_samples, n_classes)

Estimator predictions on test data. Can be probabilities (n_samples, n_classes) or class labels (n_samples,).

y_trainndarray of shape (n_samples,)

True class labels of the training data. None by default.

Returns:
ndarray of shape (n_classes,)

Class prevalence estimates.

Examples

>>> from mlquantify.counting import CC
>>> import numpy as np
>>> q = CC()
>>> predictions = np.random.rand(200)
>>> q.aggregate(predictions)
{0: 0.51, 1: 0.49}
fit(X, y, estimator_fitted=False, *args, **kwargs)[source]#

Fit the quantifier using the provided data and estimator.

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(*, estimator_fitted: bool | None | str = '$UNCHANGED$') → CC[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:
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)[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.