Crepes

Fixes

- Fixed deprecated code for checking if an array is non-empty in the `ConformalPredictiveSystem` class. Thanks to tuvelofstrom for pointing this out.

Features

- The function `margin` for computing non-conformity scores for conformal classifiers has been added to `crepes.extras`.

Fixes

- Fixed a bug in the `DifficultyEstimator` class (in `crepes.extras`), which caused an error when trying to display a non-fitted object. Thanks to tuvelofstrom for pointing this out.

- Fixed an error in the documentation for the function `hinge`.

- The Jupyter notebooks `crepes_nb_wrap.ipynb` and `crepes_nb.ipynb` have been updated to illustrate the new `margin` function.

Features

- The classes `ConformalClassifier` and `WrapClassifier` have been added to `crepes`, allowing for generation of standard and Mondrian conformal classifiers, which produce p-values and prediction sets. The `calibrate` method of `WrapClassifier` allows for easily generating class-conditional conformal classifiers and using out-of-bag calibration. See [the documentation](https://crepes.readthedocs.io/en/latest/crepes.html) for the interface to objects of the class through the `calibrate`, `predict_p` and `predict_set` methods, in addition to the `fit`, `predict` and `predict_proba` methods of the wrapped learner. The method `evaluate` allows for evaluating the predictive performance using a set of standard metrics.

- The function `hinge` for computing non-conformity scores for conformal classifiers has been added to `crepes.extras`.

Fixes

- The class `Wrap` has changed name to `WrapRegressor` and the arguments to the `calibrate` method of this class have been changed to be in line with the `calibrate` method of `WrapClassifier`.

- The Jupyter notebooks `crepes_nb_wrap.ipynb` and `crepes_nb.ipynb` have been updated and extended

Fix

- Fixed a bug in the ``evaluate`` method of ``ConformalPredictiveSystem``, which caused an error when using ``CRPS`` as a single metric, i.e., when providing ``metrics=["CRPS"]`` as input. Thanks to Zeeshan-Khaliq for pointing this out.

Feature

- The full cpds matrix is calculated only if requested to be output (``return_cpds=True``) by the ``predict`` method of ``ConformalPredictiveSystem`` or if the set of metrics include "CRPS" for the ``evaluate`` method. This allows large test and calibration sets to be handled without excessive use of memory in other cases. Thanks to christopherjluke and SebastianLeborg for highlighting and discussing the problem.

Fixes

- Default values for mandatory arguments for the methods ``fit``, ``predict`` and ``evaluate`` methods of ``ConformalRegressor`` and ``ConformalPredictiveSystem``, as well as the function ``binning`` in ``crepes.extras``, are no longer provided

- ``y_min`` and ``y_max`` correctly inserted for all percentiles

- The ``evaluate`` method for ``ConformalPredictiveSystem`` fixed to work correctly even if CRPS not included in metrics, and if all test objects belong to the same Mondrian category

- Incorrect values for percentiles will render an error message

Feature

- The class `Wrap` has been added to `crepes`, allowing for easily extending the underlying learner with methods for forming, and making predictions with, conformal regressors and predictive systems. See [the documentation](https://crepes.readthedocs.io/en/latest/crepes.html) for the interface to objects of the class through the `calibrate`, `predict_int` and `predict_cps` methods, in addition to the `fit` and `predict` methods of the wrapped learner.

Fixes

- A Jupyter notebook `crepes_nb_wrap.ipynb` has been added to the documentation to illustrate the use of the `Wrap` class.

- The output result array of a conformal predictive system is converted to a vector if the array contains one column only.

- The documentation has been updated and now includes links to classes and methods.

- `crepes.fillings` has been renamed to `crepes.extras`