|
1 | 1 | .. -*- mode: rst -*- |
2 | 2 |
|
3 | | -|Travis|_ |AppVeyor|_ |Codecov|_ |CircleCI|_ |ReadTheDocs|_ |License|_ |PythonVersion|_ |PyPi|_ |
| 3 | +|GitHubActions|_ |Codecov|_ |CircleCI|_ |ReadTheDocs|_ |License|_ |PythonVersion|_ |PyPi|_ |
4 | 4 |
|
5 | | -.. |Travis| image:: https://travis-ci.com/simai-ml/MAPIE.svg?branch=master |
6 | | -.. _Travis: https://travis-ci.com/simai-ml/MAPIE |
7 | | - |
8 | | -.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/js4d7km6ckr801nj/branch/master?svg=true |
9 | | -.. _AppVeyor: https://ci.appveyor.com/project/gmartinonQM/mapie |
| 5 | +.. |GitHubActions| image:: https://github.com/simai-ml/MAPIE/actions/workflows/test.yml/badge.svg |
| 6 | +.. _GitHubActions: https://github.com/simai-ml/MAPIE/actions |
10 | 7 |
|
11 | 8 | .. |Codecov| image:: https://codecov.io/gh/simai-ml/MAPIE/branch/master/graph/badge.svg?token=F2S6KYH4V1 |
12 | 9 | .. _Codecov: https://codecov.io/gh/simai-ml/MAPIE |
@@ -96,7 +93,7 @@ and two standard deviations from the mean. |
96 | 93 |
|
97 | 94 |
|
98 | 95 |
|
99 | | -MAPIE returns a ``np.ndarray`` of shape (n_samples, 3, len(alpha)) giving the predictions, |
| 96 | +MAPIE returns a ``np.ndarray`` of shape ``(n_samples, 3, len(alpha))`` giving the predictions, |
100 | 97 | as well as the lower and upper bounds of the prediction intervals for the target quantile |
101 | 98 | for each desired alpha value. |
102 | 99 | The estimated prediction intervals can then be plotted as follows. |
@@ -146,7 +143,18 @@ The effective coverage is the actual fraction of true labels lying in the predic |
146 | 143 | 📘 Documentation |
147 | 144 | ================ |
148 | 145 |
|
149 | | -The documentation can be found `on this link <https://mapie.readthedocs.io/en/latest/>`_. |
| 146 | +How does **MAPIE** works ? It is basically based on cross-validation and relies on: |
| 147 | + |
| 148 | +- Residuals on the whole trainig set obtained by cross-validation, |
| 149 | +- Perturbed models generated during the cross-validation. |
| 150 | + |
| 151 | +**MAPIE** then combines all these elements in a way that provides prediction intervals on new data with strong theoretical guarantees [1]. |
| 152 | + |
| 153 | +.. image:: https://github.com/simai-ml/MAPIE/raw/master/doc/images/mapie_internals.png |
| 154 | + :width: 400 |
| 155 | + :align: center |
| 156 | + |
| 157 | +The full documentation can be found `on this link <https://mapie.readthedocs.io/en/latest/>`_. |
150 | 158 | It contains the following sections: |
151 | 159 |
|
152 | 160 | - `Quickstart <https://mapie.readthedocs.io/en/latest/quick_start.html>`_ |
@@ -193,10 +201,10 @@ with the financial support from Région Ile de France. |
193 | 201 | 🔍 References |
194 | 202 | ============== |
195 | 203 |
|
196 | | -MAPIE methods are based on the work by `Foygel-Barber et al. (2020) <https://www.stat.uchicago.edu/~rina/jackknife.html>`_. |
| 204 | +MAPIE methods are based on the work by `Foygel-Barber et al. (2021) <https://doi.org/10.1214/20-AOS1965>`_. |
197 | 205 |
|
198 | 206 | [1] Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan J. Tibshirani. |
199 | | -Predictive inference with the jackknife+. Ann. Statist., 49(1):486–507, 022021 |
| 207 | +"Predictive inference with the jackknife+." Ann. Statist., 49(1):486–507, February 2021. |
200 | 208 |
|
201 | 209 | 📝 License |
202 | 210 | ========== |
|
0 commit comments