Complete documentation of the composable module

docs/source/manuals/methods.md

@@ -442,7 +442,47 @@ vary smoothing with smooth variations of this hyperparameter.
 
 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.
+The composition of a method is implemented through the [](quapy.method.composable.ComposableQuantifier) class. Its documentation also features an example to get you started in composing your own methods.
+
+```python
+ComposableQuantifier( # ordinal ACC, as proposed by Bunse et al., 2022
+  TikhonovRegularized(LeastSquaresLoss(), 0.01),
+  ClassTransformer(RandomForestClassifier(oob_score=True))
+)
+```
+
+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.
+
+### Loss functions
+
+- [](quapy.method.composable.LeastSquaresLoss)
+- [](quapy.method.composable.EnergyLoss)
+- [](quapy.method.composable.HellingerSurrogateLoss)
+- [](quapy.method.composable.BlobelLoss)
+- [](quapy.method.composable.CombinedLoss)
+
+```{hint}
+You can use the [](quapy.method.composable.CombinedLoss) to create arbitrary, weighted sums of losses and regularizers.
+```
+
+### Regularization functions
+
+- [](quapy.method.composable.TikhonovRegularized)
+- [](quapy.method.composable.TikhonovRegularization)
+
+### Feature transformations
+
+- [](quapy.method.composable.ClassTransformer)
+- [](quapy.method.composable.DistanceTransformer)
+- [](quapy.method.composable.HistogramTransformer)
+- [](quapy.method.composable.EnergyKernelTransformer)
+- [](quapy.method.composable.GaussianKernelTransformer)
+- [](quapy.method.composable.LaplacianKernelTransformer)
+- [](quapy.method.composable.GaussianRFFKernelTransformer)
+
+```{hint}
+The [](quapy.method.composable.ClassTransformer) requires the classifier to have a property `oob_score==True` and to produce a property `oob_decision_function` during fitting. In [scikit-learn](https://scikit-learn.org/), this requirement is fulfilled by any bagging classifier, such as random forests. Any other classifier needs to be cross-validated through the [](quapy.method.composable.CVClassifier).
+```
 
 
 ## Meta Models