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improved doc

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Alejandro Moreo Fernandez 2025-10-09 12:49:08 +02:00
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CHANGE_LOG.txt
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@ -1,7 +1,7 @@
Change Log 0.2.1
-----------------
...
- Improved documentation of confidence regions.
Change Log 0.2.0
-----------------

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docs/source/manuals/methods.md
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@ -604,7 +604,10 @@ estim_prevalence = model.predict(dataset.test.X)
_(New in v0.2.0!)_ Some quantification methods go beyond providing a single point estimate of class prevalence values and also produce confidence regions, which characterize the uncertainty around the point estimate. In QuaPy, two such methods are currently implemented:
* Aggregative Bootstrap: The Aggregative Bootstrap method extends any aggregative quantifier by generating confidence regions for class prevalence estimates through bootstrapping. Key features of this method include:
* Aggregative Bootstrap: The Aggregative Bootstrap method extends any aggregative quantifier by generating confidence regions for class prevalence estimates through bootstrapping. The method is described in the paper [Moreo, A., Salvati, N.
An Efficient Method for Deriving Confidence Intervals in Aggregative Quantification.
Learning to Quantify: Methods and Applications (LQ 2025), co-located at ECML-PKDD 2025.
pp 12-33, Porto (Portugal)](https://lq-2025.github.io/proceedings/CompleteVolume.pdf). Key features of this method include:
* Optimized Computation: The bootstrap is applied to pre-classified instances, significantly speeding up training and inference.
During training, bootstrap repetitions are performed only after training the classifier once. These repetitions are used to train multiple aggregation functions.

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quapy/method/confidence.py
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@ -339,6 +339,12 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
During inference, the bootstrap repetitions are applied to the pre-classified test instances.
See
`Moreo, A., Salvati, N.
An Efficient Method for Deriving Confidence Intervals in Aggregative Quantification.
Learning to Quantify: Methods and Applications (LQ 2025), co-located at ECML-PKDD 2025.
pp 12-33 <https://lq-2025.github.io/proceedings/CompleteVolume.pdf>`_
:param quantifier: an aggregative quantifier
:para n_train_samples: int, the number of training resamplings (defaults to 1, set to > 1 to activate a
model-based bootstrap approach)
@ -437,7 +443,7 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
"""
`Bayesian quantification <https://arxiv.org/abs/2302.09159>`_ method,
`Bayesian quantification <https://arxiv.org/abs/2302.09159>`_ method (by Albert Ziegler and Paweł Czyż),
which is a variant of :class:`ACC` that calculates the posterior probability distribution
over the prevalence vectors, rather than providing a point estimate obtained
by matrix inversion.