PCC#

class mlquantify.counting.PCC(estimator=None)[source]#

Probabilistic Classify and Count (PCC) quantifier.

Estimates class prevalences by averaging the posterior probabilities returned by a probabilistic classifier over all test instances. Generally less biased than CC but still not fully corrected for prior shift.

Parameters:

estimatorestimator, optional: A classifier with fit and predict_proba methods. If None, call aggregate directly.

Attributes:

estimator_estimator: The fitted underlying classifier.
classes_ndarray of shape (n_classes,): Class labels seen during fit.

References

Examples

>>> from mlquantify.counting import PCC
>>> from sklearn.linear_model import LogisticRegression
>>> import numpy as np
>>> X, y = np.random.randn(100, 5), np.random.randint(0, 2, 100)
>>> q = PCC(LogisticRegression()).fit(X, y)
>>> q.predict(X)
{0: 0.48, 1: 0.52}
>>> # call aggregate directly with pre-computed posterior probabilities
>>> proba = q.estimator_.predict_proba(X)
>>> q.aggregate(proba)
{0: 0.48, 1: 0.52}

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 PCC
>>> import numpy as np
>>> q = PCC()
>>> predictions = np.random.rand(200, 2)
>>> q.aggregate(predictions)
{0: 0.50, 1: 0.50}

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 = '$UNCHANGED$') → PCC[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.