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0.8.3 (2024--)

  • Fix conda versionning.
  • Reduce precision for test in MapieCalibrator.
  • Fix invalid certificate when downloading data.
  • Add citations utility to the documentation.
  • Add documentation for metrics.
  • Add explanation and example for symmetry argument in CQR.

0.8.3 (2024-03-01)

  • Allow the use of y and groups arguments MapieRegressor and MapieClassifier.
  • Add possibility of passing fit parameters used by estimators.
  • Fix memory issue CQR when testing for upper and lower bounds.
  • Add Winkler Interval Score.

0.8.2 (2024-01-11)

  • Resolve issue still present in 0.8.1 by updating pandas.

0.8.1 (2024-01-11)

  • First attemps at fixing library conda issue.

0.8.0 (2024-01-03)

  • Add Adaptative Conformal Inference (ACI) method for MapieTimeSeriesRegressor.
  • Add Coverage Width-based Criterion (CWC) metric.
  • Allow to use more split methods for MapieRegressor (ShuffleSplit, PredefinedSplit).
  • Allow infinite prediction intervals to be produced in regressor classes.
  • Integrate ConformityScore into MapieTimeSeriesRegressor.
  • Add (extend) the optimal estimation strategy for the bounds of the prediction intervals for regression via ConformityScore.
  • Add new checks for metrics calculations.
  • Fix reference for residual normalised score in documentation.

0.7.0 (2023-09-14)

  • Add prediction set estimation for binary classification.
  • Add Learn-Then-Test method for multilabel-classification.
  • Add documentation and notebooks for LTT.
  • Add a new conformity score, ResidualNormalisedScore, that takes X into account and allows to compute adaptive intervals.
  • Refactor MapieRegressor and ConformityScore to add the possibility to use X in ConformityScore.
  • Separate the handling of the estimator from MapieRegressor into a new class called EnsembleEstimator.
  • Rename methods (score to lac and cumulated_score to aps) in MapieClassifier.
  • Add more notebooks and examples.
  • Fix an unfixed random state in one of the classification tests.
  • Add statistical calibration tests in binary classification.
  • Fix and preserve the split behavior of the check_cv method with and without random state.

0.6.5 (2023-06-06)

  • Add grouped conditional coverage metrics named SSC for regression and classification
  • Add HSIC metric for regression
  • Migrate conformity scores classes into conformity_scores module
  • Migrate regression classes into regression module
  • Add split conformal option for regression and classification
  • Update check method for calibration
  • Fix bug in MapieClassifier with different number of labels in calibration dataset.

0.6.4 (2023-04-05)

  • Fix runtime warning with RAPS method

0.6.3 (2023-03-23)

  • Fix bug when labels do not start at 0

0.6.2 (2023-03-22)

  • Make MapieClassifier a scikit-learn object
  • Update documentation for MapieClassifier

0.6.1 (2023-01-31)

  • Fix still existing bug for classification with very low scores

0.6.0 (2023-01-19)

  • Add RCPS and CRC for multilabel-classification
  • Add Top-Label calibration
  • Fix bug for classification with very low scores

0.5.0 (2022-10-20)

  • Add RAPS method for classification
  • Add theoretical description for RAPS

0.4.2 (2022-09-02)

  • Add tutorial for time series
  • Convert existing tutorials in .py
  • Add prefit method for CQR
  • Add tutorial for CQR

0.4.1 (2022-06-27)

  • Add packaging library in requirements
  • Fix displaying problem in pypi

0.4.0 (2022-06-24)

  • Relax and fix typing
  • Add Split Conformal Quantile Regression
  • Add EnbPI method for Time Series Regression
  • Add EnbPI Documentation
  • Add example with heteroscedastic data
  • Add ConformityScore class that allows the user to define custom conformity scores

0.3.2 (2022-03-11)

  • Refactorize unit tests
  • Add "naive" and "top-k" methods in MapieClassifier
  • Include J+aB method in regression tutorial
  • Add MNIST example for classification
  • Add cross-conformal for classification
  • Add notebooks folder containing notebooks used for generating documentation tutorials
  • Uniformize the use of matrix k and add an argument "ensemble" to method "predict" in regression.py
  • Add replication of the Chen Xu's tutorial testing Jackknife+aB vs Jackknife+
  • Add Jackknife+-after-Bootstrap documentation
  • Improve scikit-learn pipelines compatibility

0.3.1 (2021-11-19)

  • Add Jackknife+-after-Bootstrap method and add mean and median as aggregation functions
  • Add "cumulative_score" method in MapieClassifier
  • Allow image as input in MapieClassifier

0.3.0 (2021-09-10)

  • Renaming estimators.py module to regression.py
  • New classification.py module with MapieClassifier class, that estimates prediction sets from softmax score
  • New set of unit tests for classification.py module
  • Modification of the documentation architecture
  • Split example gallery into separate regression and classification galleries
  • Add first classification examples
  • Add method classification_coverage_score in the module metrics.py
  • Fixed code error for plotting of interval widths in tutorial of documentation
  • Added missing import statements in tutorial of documentation
  • Refactorize tests of n_jobs and verbose in utils.py

0.2.3 (2021-07-09)

  • Inclusion in conda-forge with updated release checklist
  • Add time series example
  • Add epistemic uncertainty example
  • Remove CicleCI redundancy with ReadTheDocs
  • Remove Pep8speaks
  • Include linting in CI/CD
  • Use PyPa github actions for releases

0.2.2 (2021-06-10)

  • Set alpha parameter as predict argument, with None as default value
  • Switch to github actions for continuous integration of the code
  • Add image explaining MAPIE internals on the README

0.2.1 (2021-06-04)

  • Add cv="prefit" option
  • Add sample_weight argument in fit method

0.2.0 (2021-05-21)

  • Add n_jobs argument using joblib parallel processing
  • Allow cv to take the value -1 equivalently to LeaveOneOut()
  • Introduce the cv parameter to get closer to scikit-learn API
  • Remove the n_splits, shuffle and random_state parameters
  • Simplify the method parameter
  • Fix typos in documentation and add methods descriptions in sphinx
  • Accept alpha parameter as a list or np.ndarray. If alpha is an Iterable, .predict() returns a np.ndarray of shape (n_samples, 3, len(alpha)).

0.1.4 (2021-05-07)

  • Move all alpha related operations to predict
  • Assume default LinearRegression if estimator is None
  • Improve documentation
  • return_pred argument is now ensemble boolean

0.1.3 (2021-04-30)

  • Update PyPi homepage
  • Set up publication workflows as a github action
  • Update issue and pull request templates
  • Increase sklearn compatibility (coverage_score and unit tests)

0.1.2 (2021-04-27)

  • First release on PyPi

0.1.1 (2021-04-27)

  • First release on TestPyPi

0.1.0 (2021-04-27)

  • Implement metrics.coverage
  • Implement estimators.MapieRegressor