115 lines
7.1 KiB
Plaintext
115 lines
7.1 KiB
Plaintext
Change Log 0.1.8
|
|
----------------
|
|
|
|
- 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
|
|
|
|
- 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
|
|
|
|
- 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.
|
|
|
|
Change Log 0.1.7
|
|
----------------
|
|
|
|
- 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.
|
|
|