Installation instructions and new qunfold version

docs/source/manuals/methods.md

@@ -442,6 +442,16 @@ 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.
 
+### Installation
+
+```sh
+pip install --upgrade pip setuptools wheel
+pip install "jax[cpu]"
+pip install quapy[composable]
+```
+
+### Basics
+
 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
@@ -451,6 +461,8 @@ ComposableQuantifier( # ordinal ACC, as proposed by Bunse et al., 2022
 )
 ```
 
+More exhaustive examples of method compositions, including hyper-parameter optimization, can be found in [the example directory](https://github.com/HLT-ISTI/QuaPy/tree/master/examples).
+
 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

examples/14.composable_methods.py

@@ -127,3 +127,9 @@ print(
 
 # Note that a proper evaluation would still require the best model to be
 # evaluated on a separate test set.
+
+# To implement your own loss functions and feature representations, please
+# follow the corresponding manual of the qunfold package. This package provides
+# the back-end of QuaPy’s composable module and is fully compatible with QuaPy.
+#
+# https://mirkobunse.github.io/qunfold/developer-guide.html#custom-implementations

setup.py

@@ -125,7 +125,7 @@ setup(
     # projects.
     extras_require={  # Optional
        'bayes': ['jax', 'jaxlib', 'numpyro'],
-       'composable': ['qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.3'],
+       'composable': ['qunfold @ git+https://github.com/mirkobunse/qunfold@v0.1.4'],
        'neural': ['torch'],
        'tests': ['certifi'],
        'docs' : ['sphinx-rtd-theme', 'myst-parser'],