HLT-ISTI/QuaPy: QuaPy v0.1.8

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:

• 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).
• 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 + 1010 trainings of the aggregation function (this is typically much faster than the classifier training), whereas in versions <0.1.8 this amounted to training 1010 (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 T2A@LeQua.

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.