added new changes to log
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Change Log 0.2.1
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Change Log 0.2.1
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- Improved documentation of confidence regions.
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- Improved documentation of confidence regions. Added QuaPy logo :')
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- Added Bayesian KDEy and Bayesian MAPLS quantifiers.
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- Added Bayesian KDEy and Bayesian MAPLS quantifiers.
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- Added temperature calibration utilities for Bayesian confidence-aware methods.
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- Added temperature calibration utilities for Bayesian confidence-aware methods.
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- Added compositional CLR and ILR transformations.
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- Added compositional CLR and ILR transformations.
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- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
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- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
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- Added image-embedding-based datasets including CIFAR10, CIFAR100, CIFAR100coarse, VSHN, FashionMNIST, MNIST
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- Added image-embedding-based datasets including CIFAR10, CIFAR100, CIFAR100coarse, VSHN, FashionMNIST, MNIST.
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- Added TemperatureScalingFromLogits for calibrating pretrained logits.
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- Added TemperatureScalingFromLogits for calibrating pretrained logits.
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- Added DirichletProtocol for prevalence sampling from Dirichlet priors.
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- Added DirichletProtocol for prevalence sampling from Dirichlet priors.
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- Added ReadMe method by Daniel Hopkins and Gary King.
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- Added ReadMe method by Daniel Hopkins and Gary King.
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- Internal index in LabelledCollection is now "lazy", and is only constructed if required.
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- Internal index in LabelledCollection is now "lazy", and is only constructed if required.
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- Improved unit testing and separated integration tests.
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- Improved unit testing and separated integration tests.
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- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
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- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
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- Added visualization tools for 3-class problems in the simplex, see also the new example no.19
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- Added visualization tools for 3-class problems in the simplex, see also the new example no.19
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- Deep code revision and improved codebase
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Change Log 0.2.0
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Change Log 0.2.0
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@ -32,6 +46,7 @@ Change Log 0.2.0
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in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction
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in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction
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indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing.
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indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing.
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- This change imposes a versioning constrain with qunfold, which now must be >= 0.1.6
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- This change imposes a versioning constrain with qunfold, which now must be >= 0.1.6
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- EMQ has been modified, so that the representation function "classify" now only provides posterior
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- EMQ has been modified, so that the representation function "classify" now only provides posterior
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probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function.
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probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function.
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- A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow
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- A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow
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@ -39,13 +54,16 @@ Change Log 0.2.0
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- 'raise': raises a RuntimeException (default)
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- 'raise': raises a RuntimeException (default)
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- 'backup': reruns by silently avoiding calibration
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- 'backup': reruns by silently avoiding calibration
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- Parameter "recalib" has been renamed "calib"
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- Parameter "recalib" has been renamed "calib"
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- Added aggregative bootstrap for deriving confidence regions (confidence intervals, ellipses in the simplex, or
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- Added aggregative bootstrap for deriving confidence regions (confidence intervals, ellipses in the simplex, or
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ellipses in the CLR space). This method is efficient as it leverages the two-phases of the aggregative quantifiers.
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ellipses in the CLR space). This method is efficient as it leverages the two-phases of the aggregative quantifiers.
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This method applies resampling only to the aggregation phase, thus avoiding to train many quantifiers, or
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This method applies resampling only to the aggregation phase, thus avoiding to train many quantifiers, or
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classify multiple times the instances of a sample. See:
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classify multiple times the instances of a sample. See:
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- quapy/method/confidence.py (new)
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- quapy/method/confidence.py (new)
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- the new example no. 16.confidence_regions.py
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- the new example no. 16.confidence_regions.py
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- BayesianCC moved to confidence.py, where methods having to do with confidence intervals belong.
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- BayesianCC moved to confidence.py, where methods having to do with confidence intervals belong.
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- Improved documentation of qp.plot module.
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- Improved documentation of qp.plot module.
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@ -147,6 +165,7 @@ Change Log 0.1.8
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- New API documentation template.
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- New API documentation template.
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Change Log 0.1.7
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Change Log 0.1.7
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@ -200,7 +219,7 @@ Change Log 0.1.7
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- hyperparameters yielding to inconsistent runs raise a ValueError exception, while hyperparameter combinations
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- hyperparameters yielding to inconsistent runs raise a ValueError exception, while hyperparameter combinations
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yielding to internal errors of surrogate functions are reported and skipped, without stopping the grid search.
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yielding to internal errors of surrogate functions are reported and skipped, without stopping the grid search.
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- DistributionMatching methods added. This is a general framework for distribution matching methods that catters for
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- DistributionMatching methods added. This is a general framework for distribution matching methods that caters for
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multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
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multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
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method aligned with the Firat's formulation.
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method aligned with the Firat's formulation.
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