- Add tutorial for time series
- Convert existing tutorials in .py
- Add prefit method for CQR
- Add tutorial for CQR
- Add packaging library in requirements
- Fix displaying problem in pypi
- 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
- 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
- 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
- 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
- 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
- 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
- Add cv="prefit" option
- Add sample_weight argument in fit method
- 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)).
- Move all alpha related operations to predict
- Assume default LinearRegression if estimator is None
- Improve documentation
- return_pred argument is now ensemble boolean
- 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)
- First release on PyPi
- First release on TestPyPi
- Implement metrics.coverage
- Implement estimators.MapieRegressor