From 449618c42e6c67837699059fadf28971bde3fb51 Mon Sep 17 00:00:00 2001 From: Mirko Bunse Date: Mon, 24 Jun 2024 16:08:09 +0200 Subject: [PATCH] Documentation of the composable module --- docs/source/conf.py | 3 +++ docs/source/wiki/Methods.md | 7 +++++++ 2 files changed, 10 insertions(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 9d86c63..d863264 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -30,6 +30,7 @@ release = quapy.__version__ # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ + 'sphinx.ext.autosectionlabel', 'sphinx.ext.duration', 'sphinx.ext.doctest', 'sphinx.ext.autodoc', @@ -39,6 +40,8 @@ extensions = [ 'myst_parser', ] +autosectionlabel_prefix_document = True + source_suffix = ['.rst', '.md'] templates_path = ['_templates'] diff --git a/docs/source/wiki/Methods.md b/docs/source/wiki/Methods.md index 760df16..56d98b1 100644 --- a/docs/source/wiki/Methods.md +++ b/docs/source/wiki/Methods.md @@ -438,6 +438,13 @@ that can be explored in model selection range in [0.01, 0.25]. The methods' perf vary smoothing with smooth variations of this hyperparameter. +## Composable Methods + +The [](quapy.method.composable) module allows the composition of quantification methods from loss functions and feature transformations. Any composed method solves a linear system of equations by minimizing the loss after transforming the data. Methods of this kind include ACC, PACC, HDx, HDy, and many other well-known methods, as well as an unlimited number of re-combinations of their building blocks. + +The composition of a method is implemented through the [`ComposableQuantifier`](quapy.method.composable.ComposableQuantifier) class. Its documentation also features an example to get you started in composing your own methods. Look through the list of [module members](quapy.method.composable) to see which building blocks are already available. To implement your own loss functions and feature representations, follow the corresponding manual of the [qunfold package](https://github.com/mirkobunse/qunfold), which provides the back-end of QuaPy's composable module. + + ## Meta Models By _meta_ models we mean quantification methods that are defined on top of other