UPD: typo/warning in documentation

scikit-learn-contrib · Jan 3, 2024 · 7efe25e · 7efe25e
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diff --git a/HISTORY.rst b/HISTORY.rst
@@ -4,6 +4,9 @@ History
 
 ##### (##########)
 ------------------
+
+0.8.0 (2024-01-03)
+------------------
 * Add Adaptative Conformal Inference (ACI) method for MapieTimeSeriesRegressor.
 * Add Coverage Width-based Criterion (CWC) metric.
 * Allow to use more split methods for MapieRegressor (ShuffleSplit, PredefinedSplit).
@@ -13,7 +16,6 @@ History
 * Add new checks for metrics calculations.
 * Fix reference for residual normalised score in documentation.
 
-
 0.7.0 (2023-09-14)
 ------------------
 

diff --git a/doc/images/quickstart_1.png b/doc/images/quickstart_1.png
diff --git a/doc/index.rst b/doc/index.rst
@@ -47,6 +47,7 @@
 
    theoretical_description_multilabel_classification
    examples_multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification
+   notebooks_multilabel_classification
 
 .. toctree::
    :maxdepth: 2

diff --git a/doc/notebooks_calibration.rst b/doc/notebooks_calibration.rst
@@ -1,5 +1,5 @@
 Calibration notebooks
-========================
+=====================
 
 The following examples present advanced analyses on multi-class calibration.
 

diff --git a/doc/notebooks_multilabel_classification.rst b/doc/notebooks_multilabel_classification.rst
@@ -1,12 +1,12 @@
 Multi-label Classification notebooks
-===========================
+====================================
 
 The following examples present advanced analyses
 on multi-label classification problems with different 
 methods proposed in MAPIE.
 
 1. Overview of Recall Control for Multi-Label Classification : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/tutorial_multilabel_classification_recall.ipynb>`_
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+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 
 2. Overview of Precision Control for Multi-Label Classification : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/tutorial_multilabel_classification_precision.ipynb>`_
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+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
diff --git a/doc/notebooks_regression.rst b/doc/notebooks_regression.rst
@@ -13,6 +13,6 @@ This section lists a series of Jupyter notebooks hosted on the MAPIE Github repo
 
 
 3. Estimating prediction intervals for time series forecast with EnbPI and ACI : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/ts-changepoint.ipynb>`_
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+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 
 
diff --git a/doc/theoretical_description_calibration.rst b/doc/theoretical_description_calibration.rst
@@ -1,6 +1,6 @@
 .. title:: Theoretical Description : contents
 
-.. _theoretical_description_calibration
+.. _theoretical_description_calibration:
 
 =======================
 Theoretical Description

diff --git a/doc/theoretical_description_classification.rst b/doc/theoretical_description_classification.rst
@@ -142,7 +142,7 @@ of the last label in the prediction set. This randomization is done as follows:
 
 - First : define the :math:`V` parameter:
 .. math::
-        V_i = (s_i(X_i, Y_i) - \hat{q}_{1-\alpha}) / \left(\hat{\mu}(X_i)_{\pi_k} + \lambda \mathbb{1} (k > k_{reg})\right)
+   V_i = (s_i(X_i, Y_i) - \hat{q}_{1-\alpha}) / \left(\hat{\mu}(X_i)_{\pi_k} + \lambda \mathbb{1} (k > k_{reg})\right)
 - Compare each :math:`V_i` to :math:`U \sim` Unif(0, 1)
 - If :math:`V_i \leq U`, the last included label is removed, else we keep the prediction set as it is.
 

diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
@@ -272,7 +272,7 @@ regressions when using :class:`mapie.quantile_regression.MapieQuantileRegressor`
 
 
 10. The ensemble batch prediction intervals (EnbPI) method
-=========================================================
+==========================================================
 
 The coverage guarantee offered by the various resampling methods based on the
 jackknife strategy, and implemented in MAPIE, are only valid under the "exchangeability

diff --git a/mapie/regression/time_series_regression.py b/mapie/regression/time_series_regression.py
@@ -47,7 +47,7 @@ class MapieTimeSeriesRegressor(MapieRegressor):
     Isaac Gibbs, Emmanuel Candes
     "Adaptive conformal inference under distribution shift"
     https://proceedings.neurips.cc/paper/2021/file/\
-    0d441de75945e5acbc865406fc9a2559-Paper.pdf
+0d441de75945e5acbc865406fc9a2559-Paper.pdf
 
     Margaux Zaffran et al.
     "Adaptive Conformal Predictions for Time Series"
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Expand Up		@@ -13,6 +13,6 @@ This section lists a series of Jupyter notebooks hosted on the MAPIE Github repo


		3. Estimating prediction intervals for time series forecast with EnbPI and ACI : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/ts-changepoint.ipynb>`_
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