Merge branch 'master' into conformity_with_template_scikitlearn

CODE_OF_CONDUCT.md

@@ -60,7 +60,7 @@ representative at an online or offline event.
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement at
-tcordier@quantmetry.com.
+thibault.a.cordier@capgemini.com.
 All complaints will be reviewed and investigated promptly and fairly.
 
 All community leaders are obligated to respect the privacy and security of the

README.rst

@@ -1,36 +1,37 @@
 .. -*- mode: rst -*-
 
-|GitHubActions|_ |Codecov|_ |ReadTheDocs|_ |License|_ |PythonVersion|_ |PyPi|_ |Conda|_ |Release|_ |Commits|_ |DOI|_
+|GitHubActions| |Codecov| |ReadTheDocs| |License| |PythonVersion| |PyPi| |Conda| |Release| |Commits| |DOI|
 
 .. |GitHubActions| image:: https://github.com/scikit-learn-contrib/MAPIE/actions/workflows/test.yml/badge.svg
-.. _GitHubActions: https://github.com/scikit-learn-contrib/MAPIE/actions
+   :target: https://github.com/scikit-learn-contrib/MAPIE/actions
 
 .. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/MAPIE/branch/master/graph/badge.svg?token=F2S6KYH4V1
-.. _Codecov: https://codecov.io/gh/scikit-learn-contrib/MAPIE
+   :target: https://codecov.io/gh/scikit-learn-contrib/MAPIE
 
-.. |ReadTheDocs| image:: https://readthedocs.org/projects/mapie/badge
-.. _ReadTheDocs: https://mapie.readthedocs.io/en/latest
+.. |ReadTheDocs| image:: https://readthedocs.org/projects/mapie/badge/?version=stable
+    :target: https://mapie.readthedocs.io/en/stable/?badge=stable
+    :alt: Documentation Status
 
 .. |License| image:: https://img.shields.io/github/license/simai-ml/MAPIE
-.. _License: https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE
+   :target: https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE
 
 .. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/mapie
-.. _PythonVersion: https://pypi.org/project/mapie/
+   :target: https://pypi.org/project/mapie/
 
 .. |PyPi| image:: https://img.shields.io/pypi/v/mapie
-.. _PyPi: https://pypi.org/project/mapie/
+   :target: https://pypi.org/project/mapie/
 
 .. |Conda| image:: https://img.shields.io/conda/vn/conda-forge/mapie
-.. _Conda: https://anaconda.org/conda-forge/mapie
+   :target: https://anaconda.org/conda-forge/mapie
 
 .. |Release| image:: https://img.shields.io/github/v/release/scikit-learn-contrib/mapie
-.. _Release: https://github.com/scikit-learn-contrib/MAPIE/releases
+   :target: https://github.com/scikit-learn-contrib/MAPIE/releases
 
 .. |Commits| image:: https://img.shields.io/github/commits-since/scikit-learn-contrib/mapie/latest/master
-.. _Commits: https://github.com/scikit-learn-contrib/MAPIE/commits/master
+   :target: https://github.com/scikit-learn-contrib/MAPIE/commits/master
 
 .. |DOI| image:: https://img.shields.io/badge/10.48550/arXiv.2207.12274-B31B1B.svg
-.. _DOI: https://arxiv.org/abs/2207.12274
+   :target: https://arxiv.org/abs/2207.12274
 
 .. image:: https://github.com/simai-ml/MAPIE/raw/master/doc/images/mapie_logo_nobg_cut.png
     :width: 400
@@ -168,27 +169,27 @@ For more information on the contribution process, please go `here <CONTRIBUTING.
 MAPIE has been developed through a collaboration between Quantmetry, Michelin, ENS Paris-Saclay,
 and with the financial support from Région Ile de France and Confiance.ai.
 
-|Quantmetry|_ |Michelin|_ |ENS|_ |Confiance.ai|_  |IledeFrance|_ 
+|Quantmetry| |Michelin| |ENS| |Confiance.ai| |IledeFrance|
 
 .. |Quantmetry| image:: https://www.quantmetry.com/wp-content/uploads/2020/08/08-Logo-quant-Texte-noir.svg
-    :width: 150
-.. _Quantmetry: https://www.quantmetry.com/
+    :height: 35
+    :target: https://www.quantmetry.com/
 
-.. |Michelin| image:: https://www.michelin.com/wp-content/themes/michelin/public/img/michelin-logo-en.svg
-    :width: 100
-.. _Michelin: https://www.michelin.com/en/
+.. |Michelin| image:: https://agngnconpm.cloudimg.io/v7/https://dgaddcosprod.blob.core.windows.net/corporate-production/attachments/cls05tqdd9e0o0tkdghwi9m7n-clooe1x0c3k3x0tlu4cxi6dpn-bibendum-salut.full.png
+    :height: 35
+    :target: https://www.michelin.com/en/
 
 .. |ENS| image:: https://file.diplomeo-static.com/file/00/00/01/34/13434.svg
-    :width: 100
-.. _ENS: https://ens-paris-saclay.fr/en
+    :height: 35
+    :target: https://ens-paris-saclay.fr/en
 
 .. |Confiance.ai| image:: https://pbs.twimg.com/profile_images/1443838558549258264/EvWlv1Vq_400x400.jpg
-    :width: 100
-.. _Confiance.ai: https://www.confiance.ai/
+    :height: 35
+    :target: https://www.confiance.ai/
 
-.. |IledeFrance| image:: https://www.iledefrance.fr/themes/custom/portail_idf/logo.svg
-    :width: 100
-.. _IledeFrance: https://www.iledefrance.fr/
+.. |IledeFrance| image:: https://www.iledefrance.fr/sites/default/files/logo/2024-02/logoGagnerok.svg
+    :height: 35
+    :target: https://www.iledefrance.fr/
 
 
 🔍  References

doc/theoretical_description_binary_classification.rst

@@ -12,7 +12,7 @@ calibration (see :doc:`theoretical_description_calibration`), confidence interva
 These 3 notions are tightly related for score-based classifier, as it is shown in [1]. 
 
 Prediction sets can be computed in the same way for multiclass and binary classification with
-:class:`~mapie.calibration.MapieClassifier`, and there are the same theoretical guarantees.
+:class:`~mapie.classification.MapieClassifier`, and there are the same theoretical guarantees.
 Nevertheless, prediction sets are often much less informative in the binary case than in the multiclass case.
 
 From Gupta et al [1]:

examples/regression/2-advanced-analysis/plot_timeseries_enbpi.py

@@ -164,7 +164,7 @@
 print(
     "Coverage / prediction interval width mean for MapieTimeSeriesRegressor: "
     "\nEnbPI without any partial_fit:"
-    f"{coverage_npfit_enbpi :.3f}, {width_npfit_enbpi:.3f}"
+    f"{coverage_npfit_enbpi:.3f}, {width_npfit_enbpi:.3f}"
 )
 print(
     "Coverage / prediction interval width mean for MapieTimeSeriesRegressor: "

mapie/calibration.py

@@ -90,6 +90,25 @@ class MapieCalibrator(BaseEstimator, ClassifierMixin):
     and multiclass-to-binary reductions." arXiv preprint
     arXiv:2107.08353 (2021).
 
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from mapie.calibration import MapieCalibrator
+    >>> X_toy = np.arange(9).reshape(-1, 1)
+    >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
+    >>> mapie = MapieCalibrator().fit(X_toy, y_toy, random_state=20)
+    >>> y_calib = mapie.predict_proba(X_toy)
+    >>> print(y_calib)
+    [[0.84......        nan        nan]
+     [0.75......        nan        nan]
+     [0.62......        nan        nan]
+     [       nan 0.33......        nan]
+     [       nan 0.33......        nan]
+     [       nan 0.33......        nan]
+     [       nan        nan 0.33......]
+     [       nan        nan 0.54......]
+     [       nan        nan 0.66......]]
     """
 
     fit_attributes = [

mapie/tests/test_calibration.py

@@ -308,9 +308,9 @@ def test_correct_results(cv: str) -> None:
     )
     pred_ = mapie_cal.predict_proba(X_test)
     top_label_ece_ = top_label_ece(y_test, pred_)
-    np.testing.assert_array_almost_equal(  # type:ignore
-        results[cv]["y_score"],
-        pred_,
+    np.testing.assert_array_almost_equal(
+        np.array(results[cv]["y_score"]),
+        np.array(pred_),
         decimal=2
     )
     np.testing.assert_allclose(  # type:ignore
@@ -341,9 +341,9 @@ def test_correct_results_binary(cv: str) -> None:
     pred_ = mapie_cal.predict_proba(X_binary)
     top_label_ece_ = top_label_ece(y_binary, pred_)
     ece = expected_calibration_error(y_binary, pred_)
-    np.testing.assert_array_almost_equal(  # type:ignore
-        results_binary[cv]["y_score"],
-        pred_,
+    np.testing.assert_array_almost_equal(
+        np.array(results_binary[cv]["y_score"]),
+        np.array(pred_),
         decimal=2
     )
     np.testing.assert_allclose(  # type:ignore