added new changes to log

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Alejandro Moreo Fernandez 2026-07-04 19:20:21 +02:00
parent ed02be2c8d
commit c59ee76d9d
1 changed files with 22 additions and 3 deletions

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Change Log 0.2.1 Change Log 0.2.1
----------------- -----------------
- Improved documentation of confidence regions. - Improved documentation of confidence regions. Added QuaPy logo :')
- Added Bayesian KDEy and Bayesian MAPLS quantifiers. - Added Bayesian KDEy and Bayesian MAPLS quantifiers.
- Added temperature calibration utilities for Bayesian confidence-aware methods. - Added temperature calibration utilities for Bayesian confidence-aware methods.
- Added compositional CLR and ILR transformations. - Added compositional CLR and ILR transformations.
- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability. - Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
- Added image-embedding-based datasets including CIFAR10, CIFAR100, CIFAR100coarse, VSHN, FashionMNIST, MNIST
- Added image-embedding-based datasets including CIFAR10, CIFAR100, CIFAR100coarse, VSHN, FashionMNIST, MNIST.
- Added TemperatureScalingFromLogits for calibrating pretrained logits. - Added TemperatureScalingFromLogits for calibrating pretrained logits.
- Added DirichletProtocol for prevalence sampling from Dirichlet priors. - Added DirichletProtocol for prevalence sampling from Dirichlet priors.
- Added ReadMe method by Daniel Hopkins and Gary King. - Added ReadMe method by Daniel Hopkins and Gary King.
- Internal index in LabelledCollection is now "lazy", and is only constructed if required. - Internal index in LabelledCollection is now "lazy", and is only constructed if required.
- Improved unit testing and separated integration tests. - Improved unit testing and separated integration tests.
- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method. - Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
- Added visualization tools for 3-class problems in the simplex, see also the new example no.19 - Added visualization tools for 3-class problems in the simplex, see also the new example no.19
- Deep code revision and improved codebase
Change Log 0.2.0 Change Log 0.2.0
----------------- -----------------
@ -32,6 +46,7 @@ Change Log 0.2.0
in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction
indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing. indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing.
- This change imposes a versioning constrain with qunfold, which now must be >= 0.1.6 - This change imposes a versioning constrain with qunfold, which now must be >= 0.1.6
- EMQ has been modified, so that the representation function "classify" now only provides posterior - EMQ has been modified, so that the representation function "classify" now only provides posterior
probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function. probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function.
- A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow - A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow
@ -39,13 +54,16 @@ Change Log 0.2.0
- 'raise': raises a RuntimeException (default) - 'raise': raises a RuntimeException (default)
- 'backup': reruns by silently avoiding calibration - 'backup': reruns by silently avoiding calibration
- Parameter "recalib" has been renamed "calib" - Parameter "recalib" has been renamed "calib"
- Added aggregative bootstrap for deriving confidence regions (confidence intervals, ellipses in the simplex, or - Added aggregative bootstrap for deriving confidence regions (confidence intervals, ellipses in the simplex, or
ellipses in the CLR space). This method is efficient as it leverages the two-phases of the aggregative quantifiers. ellipses in the CLR space). This method is efficient as it leverages the two-phases of the aggregative quantifiers.
This method applies resampling only to the aggregation phase, thus avoiding to train many quantifiers, or This method applies resampling only to the aggregation phase, thus avoiding to train many quantifiers, or
classify multiple times the instances of a sample. See: classify multiple times the instances of a sample. See:
- quapy/method/confidence.py (new) - quapy/method/confidence.py (new)
- the new example no. 16.confidence_regions.py - the new example no. 16.confidence_regions.py
- BayesianCC moved to confidence.py, where methods having to do with confidence intervals belong. - BayesianCC moved to confidence.py, where methods having to do with confidence intervals belong.
- Improved documentation of qp.plot module. - Improved documentation of qp.plot module.
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- New API documentation template. - New API documentation template.
Change Log 0.1.7 Change Log 0.1.7
---------------- ----------------
@ -200,7 +219,7 @@ Change Log 0.1.7
- hyperparameters yielding to inconsistent runs raise a ValueError exception, while hyperparameter combinations - 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. 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 - DistributionMatching methods added. This is a general framework for distribution matching methods that caters for
multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
method aligned with the Firat's formulation. method aligned with the Firat's formulation.