PCC#

class mlquantify.adjust_counting.PCC(learner=None)[source]#

Probabilistic Classify and Count (PCC) quantifier.

Implements the Probabilistic Classify and Count method for quantification as described in: [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.

Parameters:

learnerestimator, optional: A supervised learning estimator with fit and predict_proba methods. If None, it is expected that will be used the aggregate method directly.

Attributes:

learnerestimator: Underlying classification model.
classesndarray of shape (n_classes,): Unique class labels observed during training.

Examples

>>> from mlquantify.adjust_counting import PCC
>>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> X = np.random.randn(100, 5)
>>> y = np.random.randint(0, 2, 100)
>>> q = PCC(learner=LogisticRegression())
>>> q.fit(X, y)
>>> q.predict(X)
{0: 0.48, 1: 0.52}
>>> q2 = PCC()
>>> predictions = np.random.rand(200, 2)
>>> q2.aggregate(predictions)
{0: 0.50, 1: 0.50}

aggregate(predictions)[source]#: Aggregate predictions into class prevalence estimates.

fit(X, y, learner_fitted=False, *args, **kwargs)[source]#: Fit the quantifier using the provided data and learner.

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(*, learner_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:

learner_fittedstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for learner_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.

PCC#

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