Merge pull request #166 from scikit-learn-contrib/exoplanet-notebook

Exoplanet notebook

Author: tmorzade

doc/Cifar10.rst

@@ -15,6 +15,7 @@
    theoretical_description_regression
    tutorial_regression
    examples_regression/index
+   notebooks_regression
 
 .. toctree::
    :maxdepth: 2

doc/index.rst

@@ -1,13 +1,10 @@
-Classification Notebooks
+Classification notebooks
 ========================
 
-The following examples present you advanced analyses on multi-class classification
+The following examples present advanced analyses on multi-class classification
 problems for computer vision settings that are too heavy to be included in the example
 galleries.
 
 
-
-.. toctree::
-   :hidden:
-
-   Cifar10
+   1. Estimating prediction sets on the Cifar10 dataset : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/Cifar10.ipynb>`_
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------

doc/notebooks_classification.rst

@@ -0,0 +1,14 @@
+Regression notebooks
+====================
+
+This section lists a series of Jupyter notebooks hosted on the MAPIE Github repository that can be run on Google Colab.
+
+
+1. Estimating the uncertainties in the exoplanet masses : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/exoplanets.ipynb>`_
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+
+2. Estimating prediction intervals for time series forecast with EnbPI : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/ts-changepoint.ipynb>`_
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+
+

doc/notebooks_regression.rst

@@ -9,12 +9,13 @@ dependencies:
     - jupytext=1.13.6
     - matplotlib=3.5.1
     - nbconvert=6.4.0
+    - numpy=1.22.3
     - pandas=1.3.5
     - pip=22.0.3
     - pip:
         - scikeras==0.4.1
     - python=3.10
     - scikit-learn=1.0.1
+    - seaborn=0.11.2
     - tensorflow=2.7.0
     - xgboost=1.5.1
-    - numpy=1.22.3