Quapy

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- Added Kernel Density Estimation methods (KDEyML, KDEyCS, KDEyHD) as proposed in the paper:
Moreo, A., González, P., & del Coz, J. J. Kernel Density Estimation for Multiclass Quantification.
arXiv preprint arXiv:2401.00490, 2024

- Substantial internal refactor: aggregative methods now inherit a pattern by which the fit method consists of:
a) fitting the classifier and returning the representations of the training instances (typically the posterior
probabilities, the label predictions, or the classifier scores, and typically obtained through kFCV).
b) fitting an aggregation function
The function implemented in step a) is inherited from the super class. Each new aggregative method now has to
implement only the "aggregative_fit" of step b).
This pattern was already implemented for the prediction (thus allowing evaluation functions to be performed
very quicky), and is now available also for training. The main benefit is that model selection now can nestle
the training of quantifiers in two levels: one for the classifier, and another for the aggregation function.
As a result, a method with a param grid of 10 combinations for the classifier and 10 combinations for the
quantifier, now implies 10 trainings of the classifier + 10*10 trainings of the aggregation function (this is
typically much faster than the classifier training), whereas in versions <0.1.8 this amounted to training
10*10 (classifiers+aggregations).

- Added different solvers for ACC and PACC quantifiers. In quapy < 0.1.8 these quantifiers try to solve the system
of equations Ax=B exactly (by means of np.linalg.solve). As noted by Mirko Bunse (thanks!), such an exact solution
does sometimes not exist. In cases like this, quapy < 0.1.8 resorted to CC for providing a plausible solution.
ACC and PACC now resorts to an approximated solution in such cases (minimizing the L2-norm of the difference
between Ax-B) as proposed by Mirko Bunse. A quick experiment reveals this heuristic greatly improves the results
of ACC and PACC in T2ALeQua.

- Fixed ThresholdOptimization methods (X, T50, MAX, MS and MS2). Thanks to Tobias Schumacher and colleagues for pointing
this out in Appendix A of "Schumacher, T., Strohmaier, M., & Lemmerich, F. (2021). A comparative evaluation of
quantification methods. arXiv:2103.03223v3 [cs.LG]"

- Added HDx and DistributionMatchingX to non-aggregative quantifiers (see also the new example "comparing_HDy_HDx.py")

- New UCI multiclass datasets added (thanks to Pablo González). The 5 UCI multiclass datasets are those corresponding
to the following criteria:
- >1000 instances
- >2 classes
- classification datasets
- Python API available

- New IFCB (plankton) dataset added (thanks to Pablo González). See qp.datasets.fetch_IFCB.

- Added new evaluation measures NAE, NRAE (thanks to Andrea Esuli)

- Added new meta method "MedianEstimator"; an ensemble of binary base quantifiers that receives as input a dictionary
of hyperparameters that will explore exhaustively, fitting and generating predictions for each combination of
hyperparameters, and that returns, as the prevalence estimates, the median across all predictions.

- Added "custom_protocol.py" example.

- New API documentation template.

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- Protocols are now abstracted as instances of AbstractProtocol. There is a new class extending AbstractProtocol called
AbstractStochasticSeededProtocol, which implements a seeding policy to allow replicate the series of samplings.
There are some examples of protocols, APP, NPP, UPP, DomainMixer (experimental).
The idea is to start the sample generation by simply calling the __call__ method.
This change has a great impact in the framework, since many functions in qp.evaluation, qp.model_selection,
and sampling functions in LabelledCollection relied of the old functions. E.g., the functionality of
qp.evaluation.artificial_prevalence_report or qp.evaluation.natural_prevalence_report is now obtained by means of
qp.evaluation.report which takes a protocol as an argument. I have not maintained compatibility with the old
interfaces because I did not really like them. Check the wiki guide and the examples for more details.

- Exploration of hyperparameters in Model selection can now be run in parallel (there was a n_jobs argument in
QuaPy 0.1.6 but only the evaluation part for one specific hyperparameter was run in parallel).

- The prediction function has been refactored, so it applies the optimization for aggregative quantifiers (that
consists in pre-classifying all instances, and then only invoking aggregate on the samples) only in cases in
which the total number of classifications would be smaller than the number of classifications with the standard
procedure. The user can now specify "force", "auto", True of False, in order to actively decide for applying it
or not.

- examples directory created!

- DyS, Topsoe distance and binary search (thanks to Pablo González)

- Multi-thread reproducibility via seeding (thanks to Pablo González)

- n_jobs is now taken from the environment if set to None

- ACC, PACC, Forman's threshold variants have been parallelized.

- cross_val_predict (for quantification) added to model_selection: would be nice to allow the user specifies a
test protocol maybe, or None for bypassing it?

- Bugfix: adding two labelled collections (with +) now checks for consistency in the classes

- newer versions of numpy raise a warning when accessing types (e.g., np.float). I have replaced all such instances
with the plain python type (e.g., float).

- new dependency "abstention" (to add to the project requirements and setup). Calibration methods from
https://github.com/kundajelab/abstention added.

- the internal classifier of aggregative methods is now called "classifier" instead of "learner"

- when optimizing the hyperparameters of an aggregative quantifier, the classifier's specific hyperparameters
should be marked with a "classifier__" prefix (just like in scikit-learn with estimators), while the quantifier's
specific hyperparameters are named directly. For example, PCC(LogisticRegression()) quantifier has hyperparameters
"classifier__C", "classifier__class_weight", etc., instead of "C" and "class_weight" as in v0.1.6.

- hyperparameters yielding to inconsistent runs raise a ValueError exception, while hyperparameter combinations
yielding to internal errors of surrogate functions are reported and skipped, without stopping the grid search.

- DistributionMatching methods added. This is a general framework for distribution matching methods that catters for
multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
method aligned with the Firat's formulation.

- internal method properties "binary", "aggregative", and "probabilistic" have been removed; these conditions are
checked via isinstance

- quantifiers (i.e., classes that inherit from BaseQuantifier) are not forced to implement classes_ or n_classes;
these can be used anyway internally, but the framework will not suppose (nor impose) that a quantifier implements
them

- qp.evaluation.prediction has been optimized so that, if a quantifier is of type aggregative, and if the evaluation
protocol is of type OnLabelledCollection, then the computation is faster. In this specific case, the predictions
are issued only once and for all, and not for each sample. An exception to this (which is implement also), is
when the number of instances across all samples is anyway smaller than the number of instances in the original
labelled collection; in this case the heuristic is of no help, and is therefore not applied.

- the distinction between "classify" and "posterior_probabilities" has been removed in Aggregative quantifiers,
so that probabilistic classifiers return posterior probabilities, while non-probabilistic quantifiers
return crisp decisions.

- OneVsAll fixed. There are now two classes: a generic one OneVsAllGeneric that works with any quantifier (e.g.,
any instance of BaseQuantifier), and a subclass of it called OneVsAllAggregative which implements the
classify / aggregate interface. Both are instances of OneVsAll. There is a method getOneVsAll that returns the
best instance based on the type of quantifier.