merge from devel

Author: AlexMoreo

CHANGE_LOG.txt

@@ -1,3 +1,10 @@
+Change Log 0.2.2
+-----------------
+
+- Added HistNetQ, based on the original implementation https://github.com/pglez84/histnetq
+
+- Minor fixes
+
 Change Log 0.2.1
 -----------------
 

TODO.txt

@@ -1,22 +1,20 @@
-Solve the warnings issue; right now there is a warning ignore in method/__init__.py:
-
 Add 'platt' to calib options in EMQ?
 
 Allow n_prevpoints in APP to be specified by a user-defined grid?
 
-Add the fix suggested by Alexander?
-"For a more general application, I would maybe first establish a per-class threshold value of plausible prevalence
+Add the fix suggested by Alexander:
+
+For a more general application, I would maybe first establish a per-class threshold value of plausible prevalence
 based on the number of actual positives and the required sample size; e.g., for sample_size=100 and actual
 positives [10, 100, 500] -> [0.1, 1.0, 1.0], meaning that class 0 can be sampled at most at 0.1 prevalence, while
 the others can be sampled up to 1. prevalence. Then, when a prevalence value is requested, e.g., [0.33, 0.33, 0.33],
 we may either clip each value and normalize (as you suggest for the extreme case, e.g., [0.1, 0.33, 0.33]/sum) or
-scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum."
+scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum.
 - This affects LabelledCollection
 - This functionality should be accessible via sampling protocols and evaluation functions
 
-- [TODO] document confidence in manuals
+- [TODO] Test the return_type="index" in protocols and finish the "distributing_samples.py" example
 - [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
-- [TODO] add HistNetQ
 - [TODO] add CDE-iteration and Bayes-CDE methods
 - [TODO] add Friedman's method and DeBias
 - [TODO] check ignore warning stuff

docs/source/manuals/methods.md

@@ -851,7 +851,36 @@ model.fit(*dataset.training.Xy)
 estim_prevalence = model.predict(dataset.test.X)
 ```
 
-(confidence-regions-for-class-prevalence-estimation)=
+### HistNetQ
+
+QuaPy offers an implementation of HistNetQ, a deep learning model based on a differentiable
+histogram representation, presented in:
+
+[_Pérez-Mon, O., Moreo, A., del Coz, J.J., & González, P. (2025).
+Quantification using permutation-invariant networks based on histograms.
+Neural Computing and Applications, 37(5), 3505-3520._](https://doi.org/10.1007/s00521-024-10721-1)
+
+This model requires `torch` to be installed. Like QuaNet, HistNetQ is trained end-to-end on
+samples ("bags") of known prevalence rather than on individually labeled instances; unlike QuaNet,
+it requires no classifier at all, only an optional feature extraction module (a plain identity
+module is used by default, for already-vectorized data).
+
+```python
+import quapy as qp
+from quapy.method.meta import HistNetQ
+
+dataset = qp.datasets.fetch_UCIBinaryDataset('haberman')
+
+model = HistNetQ(bag_size=100, device='cpu')
+model.fit(*dataset.training.Xy)
+estim_prevalence = model.predict(dataset.test.X)
+```
+
+HistNetQ can alternatively be trained directly from a protocol that already provides the training
+samples (e.g., when only bag-level prevalence values are available), via the `fit_from_samples`
+method; see the API documentation for further details.
+
+
 ## Quantifiers with Uncertainty Quantification
 
 _(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 families are currently implemented: bootstrap methods and Bayesian methods.

docs/source/quapy.method.rst

@@ -17,7 +17,7 @@ quapy.method.aggregative module
    :undoc-members:
    :show-inheritance:
 
-.. automodule:: quapy.method._neural
+.. automodule:: quapy.method._quanet
    :members:
    :undoc-members:
    :show-inheritance:

quapy/__init__.py

@@ -17,7 +17,7 @@
 except ImportError:
     plot = None
 
-__version__ = '0.2.1.post1'
+__version__ = '0.2.2'
 
 
 def _default_cls():

quapy/method/__init__.py

@@ -74,7 +74,8 @@
 
 META_METHODS = {
     meta.Ensemble,
-    meta.QuaNet
+    meta.QuaNet,
+    meta.HistNetQ
 }
 
 QUANTIFICATION_METHODS = AGGREGATIVE_METHODS | NON_AGGREGATIVE_METHODS | META_METHODS