From 1fb8500e8765754f69381660749ce06b9b640b92 Mon Sep 17 00:00:00 2001 From: Alejandro Moreo Date: Thu, 9 Oct 2025 12:49:08 +0200 Subject: [PATCH] improved doc --- CHANGE_LOG.txt | 2 +- docs/source/manuals/methods.md | 5 ++++- quapy/method/confidence.py | 8 +++++++- 3 files changed, 12 insertions(+), 3 deletions(-) diff --git a/CHANGE_LOG.txt b/CHANGE_LOG.txt index b114e46..3e5155f 100644 --- a/CHANGE_LOG.txt +++ b/CHANGE_LOG.txt @@ -1,7 +1,7 @@ Change Log 0.2.1 ----------------- -... +- Improved documentation of confidence regions. Change Log 0.2.0 ----------------- diff --git a/docs/source/manuals/methods.md b/docs/source/manuals/methods.md index 47b7cad..93aa1dd 100644 --- a/docs/source/manuals/methods.md +++ b/docs/source/manuals/methods.md @@ -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. diff --git a/quapy/method/confidence.py b/quapy/method/confidence.py index f54768c..7f70bb8 100644 --- a/quapy/method/confidence.py +++ b/quapy/method/confidence.py @@ -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 `_ + :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 `_ method, + `Bayesian quantification `_ 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.