scikit-learn · thomasjpfan · Oct 20, 2021 · Sep 21, 2021 · Sep 21, 2021 · Sep 21, 2021
diff --git a/doc/about.rst b/doc/about.rst
@@ -85,8 +85,8 @@ citations to the following paper:
 If you want to cite scikit-learn for its API or design, you may also want to consider the
 following paper:
 
-  `API design for machine learning software: experiences from the scikit-learn
-  project <https://arxiv.org/abs/1309.0238>`_, Buitinck *et al.*, 2013.
+  :arxiv:`API design for machine learning software: experiences from the scikit-learn
+  project <1309.0238>`, Buitinck *et al.*, 2013.
 
   Bibtex entry::
 

diff --git a/doc/conf.py b/doc/conf.py
@@ -48,6 +48,7 @@
     "add_toctree_functions",
     "sphinx-prompt",
     "sphinxext.opengraph",
+    "doi_role",
 ]
 
 # Support for `plot::` directives in sphinx 3.2 requires matplotlib 3.1.0 or newer

diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
@@ -544,9 +544,9 @@ graph, and SpectralClustering is initialized with `affinity='precomputed'`::
    <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100>`_
    Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001
 
- * `"Preconditioned Spectral Clustering for Stochastic
+ * :arxiv:`"Preconditioned Spectral Clustering for Stochastic
    Block Partition Streaming Graph Challenge"
-   <https://arxiv.org/abs/1708.07481>`_
+   <1309.0238>`
    David Zhuzhunashvili, Andrew Knyazev
 
 .. _hierarchical_clustering:
@@ -1702,10 +1702,9 @@ cluster analysis.
 
 .. topic:: References
 
- * Peter J. Rousseeuw (1987). "Silhouettes: a Graphical Aid to the
-   Interpretation and Validation of Cluster Analysis". Computational
-   and Applied Mathematics 20: 53–65.
-   `doi:10.1016/0377-0427(87)90125-7 <https://doi.org/10.1016/0377-0427(87)90125-7>`_.
+ * Peter J. Rousseeuw (1987). :doi:`"Silhouettes: a Graphical Aid to the
+   Interpretation and Validation of Cluster Analysis"<10.1016/0377-0427(87)90125-7>`
+   . Computational and Applied Mathematics 20: 53–65.
 
 
 Advantages
@@ -1804,8 +1803,7 @@ number of points in cluster :math:`q`.
  * Caliński, T., & Harabasz, J. (1974).
    `"A Dendrite Method for Cluster Analysis"
    <https://www.researchgate.net/publication/233096619_A_Dendrite_Method_for_Cluster_Analysis>`_.
-   Communications in Statistics-theory and Methods 3: 1-27.
-   `doi:10.1080/03610927408827101 <https://doi.org/10.1080/03610927408827101>`_.
+   :doi:`Communications in Statistics-theory and Methods 3: 1-27 <10.1080/03610927408827101>`.
 
 
 .. _davies-bouldin_index:
@@ -1880,15 +1878,13 @@ Then the Davies-Bouldin index is defined as:
 .. topic:: References
 
  * Davies, David L.; Bouldin, Donald W. (1979).
-   "A Cluster Separation Measure"
+   :doi:`"A Cluster Separation Measure" <10.1109/TPAMI.1979.4766909>`
    IEEE Transactions on Pattern Analysis and Machine Intelligence.
    PAMI-1 (2): 224-227.
-   `doi:10.1109/TPAMI.1979.4766909 <https://doi.org/10.1109/TPAMI.1979.4766909>`_.
 
  * Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (2001).
-   "On Clustering Validation Techniques"
+   :doi:`"On Clustering Validation Techniques" <10.1023/A:1012801612483>`
    Journal of Intelligent Information Systems, 17(2-3), 107-145.
-   `doi:10.1023/A:1012801612483 <https://doi.org/10.1023/A:1012801612483>`_.
 
  * `Wikipedia entry for Davies-Bouldin index
    <https://en.wikipedia.org/wiki/Davies–Bouldin_index>`_.

diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst
@@ -167,9 +167,9 @@ Note: the implementation of ``inverse_transform`` in :class:`PCA` with
 .. topic:: References:
 
     * Algorithm 4.3 in
-      `"Finding structure with randomness: Stochastic algorithms for
+      :arxiv:`"Finding structure with randomness: Stochastic algorithms for
       constructing approximate matrix decompositions"
-      <https://arxiv.org/abs/0909.4061>`_
+      <0909.4061>`
       Halko, et al., 2009
 
     * `"An implementation of a randomized algorithm for principal component
@@ -341,13 +341,13 @@ components is less than 10 (strict) and the number of samples is more than 200
 
     * *randomized* solver:
 
-        - Algorithm 4.3 in
-          `"Finding structure with randomness: Stochastic algorithms for
+        * Algorithm 4.3 in
+          :arxiv:`"Finding structure with randomness: Stochastic algorithms for
           constructing approximate matrix decompositions"
-          <https://arxiv.org/abs/0909.4061>`_
+          <0909.4061>`
           Halko, et al., 2009
 
-        - `"An implementation of a randomized algorithm for principal component
+        * `"An implementation of a randomized algorithm for principal component
           analysis"
           <https://arxiv.org/pdf/1412.3510.pdf>`_
           A. Szlam et al. 2014

diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
@@ -1210,8 +1210,8 @@ Finally, many parts of the implementation of
      <https://statweb.stanford.edu/~jhf/ftp/stobst.pdf>`_
   .. [R2007] G. Ridgeway, "Generalized Boosted Models: A guide to the gbm
      package", 2007
-  .. [XGBoost] Tianqi Chen, Carlos Guestrin, `"XGBoost: A Scalable Tree
-     Boosting System" <https://arxiv.org/abs/1603.02754>`_
+  .. [XGBoost] Tianqi Chen, Carlos Guestrin, :arxiv:`"XGBoost: A Scalable Tree
+     Boosting System" <1603.02754>`
   .. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient
      BoostingDecision Tree" <https://papers.nips.cc/paper/
      6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`_

diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst
@@ -538,9 +538,8 @@ since it has reached the last iteration (3) with the highest score:
        Optimization <http://proceedings.mlr.press/v51/jamieson16.html>`_, in
        proc. of Machine Learning Research, 2016.
     .. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar,
-       `Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
-       <https://arxiv.org/abs/1603.06560>`_, in Machine Learning Research
-       18, 2018.
+       :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
+       <1603.06560>`, in Machine Learning Research 18, 2018.
 
 .. _grid_search_tips:
 

diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst
@@ -246,8 +246,8 @@ or store training examples.
     .. [VVZ2010] `"Generalized RBF feature maps for Efficient Detection"
       <https://www.robots.ox.ac.uk/~vgg/publications/2010/Sreekanth10/sreekanth10.pdf>`_
       Vempati, S. and Vedaldi, A. and Zisserman, A. and Jawahar, CV - 2010
-    .. [PP2013] `"Fast and scalable polynomial kernels via explicit feature maps"
-      <https://doi.org/10.1145/2487575.2487591>`_
+    .. [PP2013] :doi:`"Fast and scalable polynomial kernels via explicit feature maps"
+      <10.1145/2487575.2487591>`
       Pham, N., & Pagh, R. - 2013
     .. [CCF2002] `"Finding frequent items in data streams"
       <http://www.cs.princeton.edu/courses/archive/spring04/cos598B/bib/CharikarCF.pdf>`_

diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
@@ -934,7 +934,9 @@ to warm-starting (see :term:`Glossary <warm_start>`).
 
     .. [6] Mark Schmidt, Nicolas Le Roux, and Francis Bach: `Minimizing Finite Sums with the Stochastic Average Gradient. <https://hal.inria.fr/hal-00860051/document>`_
 
-    .. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien: `SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives. <https://arxiv.org/abs/1407.0202>`_
+    .. [7] Aaron Defazio, Francis Bach, Simon Lacoste-Julien: 
+        :arxiv:`SAGA: A Fast Incremental Gradient Method With Support for 
+        Non-Strongly Convex Composite Objectives. <1407.0202>`
 
     .. [8] https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm
 
@@ -1492,12 +1494,12 @@ problem. The current implementation is based on
     <https://gib.people.uic.edu/RQ.pdf>`_
     Econometrica: journal of the Econometric Society, 33-50.
 
-  * Portnoy, S., & Koenker, R. (1997). The Gaussian hare and the Laplacian
+  * Portnoy, S., & Koenker, R. (1997). :doi:`The Gaussian hare and the Laplacian
     tortoise: computability of squared-error versus absolute-error estimators.
-    Statistical Science, 12, 279-300. https://doi.org/10.1214/ss/1030037960
+    Statistical Science, 12, 279-300 <10.1214/ss/1030037960>`.
 
-  * Koenker, R. (2005). Quantile Regression.
-    Cambridge University Press. https://doi.org/10.1017/CBO9780511754098
+  * Koenker, R. (2005). :doi:`Quantile Regression <10.1017/CBO9780511754098>`.
+    Cambridge University Press.
 
 
 .. _polynomial_regression:

diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
@@ -563,8 +563,9 @@ or *informedness*.
      Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples,
      and Case Studies <https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics>`_,
      2015.
-  .. [Urbanowicz2015] Urbanowicz R.J.,  Moore, J.H. `ExSTraCS 2.0: description and evaluation of a scalable learning
-     classifier system <https://doi.org/10.1007/s12065-015-0128-8>`_, Evol. Intel. (2015) 8: 89.
+  .. [Urbanowicz2015] Urbanowicz R.J.,  Moore, J.H. :doi:`ExSTraCS 2.0: description 
+      and evaluation of a scalable learning classifier 
+      system <10.1007/s12065-015-0128-8>`, Evol. Intel. (2015) 8: 89.
 
 .. _cohen_kappa:
 

diff --git a/doc/modules/neural_networks_supervised.rst b/doc/modules/neural_networks_supervised.rst
@@ -184,7 +184,7 @@ Algorithms
 
 MLP trains using `Stochastic Gradient Descent
 <https://en.wikipedia.org/wiki/Stochastic_gradient_descent>`_,
-`Adam <https://arxiv.org/abs/1412.6980>`_, or
+:arxiv:`Adam <1412.6980>`, or
 `L-BFGS <https://en.wikipedia.org/wiki/Limited-memory_BFGS>`__.
 Stochastic Gradient Descent (SGD) updates parameters using the gradient of the
 loss function with respect to a parameter that needs adaptation, i.e.

diff --git a/doc/modules/partial_dependence.rst b/doc/modules/partial_dependence.rst
@@ -262,8 +262,8 @@ estimators that support it, and 'brute' is used for the rest.
     C. Molnar, `Interpretable Machine Learning
     <https://christophm.github.io/interpretable-ml-book/>`_, Section 5.1, 2019.
 
-    A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, `Peeking Inside the
+    A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, :arxiv:`Peeking Inside the
     Black Box: Visualizing Statistical Learning With Plots of Individual
-    Conditional Expectation <https://arxiv.org/abs/1309.6392>`_,
+    Conditional Expectation <1309.6392>`,
     Journal of Computational and Graphical Statistics, 24(1): 44-65, Springer,
     2015.
diff --git a/doc/modules/permutation_importance.rst b/doc/modules/permutation_importance.rst
@@ -184,5 +184,5 @@ example:
 
 .. topic:: References:
 
-   .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32,
-       2001. https://doi.org/10.1023/A:1010933404324
+   .. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`, 
+      Machine Learning, 45(1), 5-32, 2001.
diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst
@@ -977,14 +977,12 @@ Interestingly, a :class:`SplineTransformer` of ``degree=0`` is the same as
 
 .. topic:: References:
 
-    * Eilers, P., & Marx, B. (1996). Flexible Smoothing with B-splines and
-      Penalties. Statist. Sci. 11 (1996), no. 2, 89--121.
-      `doi:10.1214/ss/1038425655 <https://doi.org/10.1214/ss/1038425655>`_
-
-    * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. A review of
-      spline function procedures in R. BMC Med Res Methodol 19, 46 (2019).
-      `doi:10.1186/s12874-019-0666-3
-      <https://doi.org/10.1186/s12874-019-0666-3>`_
+    * Eilers, P., & Marx, B. (1996). :doi:`Flexible Smoothing with B-splines and
+      Penalties <10.1214/ss/1038425655>`. Statist. Sci. 11 (1996), no. 2, 89--121.
+
+    * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. :doi:`A review of
+      spline function procedures in R <10.1186/s12874-019-0666-3>`. 
+      BMC Med Res Methodol 19, 46 (2019).
 
 .. _function_transformer:
 

diff --git a/doc/sphinxext/doi_role.py b/doc/sphinxext/doi_role.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+"""
+    doilinks
+    ~~~~~~~~
+    Extension to add links to DOIs. With this extension you can use e.g.
+    :doi:`10.1016/S0022-2836(05)80360-2` in your documents. This will
+    create a link to a DOI resolver
+    (``https://doi.org/10.1016/S0022-2836(05)80360-2``).
+    The link caption will be the raw DOI.
+    You can also give an explicit caption, e.g.
+    :doi:`Basic local alignment search tool <10.1016/S0022-2836(05)80360-2>`.
+
+    :copyright: Copyright 2015  Jon Lund Steffensen. Based on extlinks by
+        the Sphinx team.
+    :license: BSD.
+"""
+
+from docutils import nodes, utils
+
+from sphinx.util.nodes import split_explicit_title
+
+
+def reference_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):
+    text = utils.unescape(text)
+    has_explicit_title, title, part = split_explicit_title(text)
+    if typ in ["arXiv", "arxiv"]:
+        full_url = "https://arxiv.org/abs/" + part
+        if not has_explicit_title:
+            title = "arXiv:" + part
+        pnode = nodes.reference(title, title, internal=False, refuri=full_url)
+        return [pnode], []
+    if typ in ["doi", "DOI"]:
+        full_url = "https://doi.org/" + part
+        if not has_explicit_title:
+            title = "DOI:" + part
+        pnode = nodes.reference(title, title, internal=False, refuri=full_url)
+        return [pnode], []
+
+
+def setup_link_role(app):
+    app.add_role("arxiv", reference_role, override=True)
+    app.add_role("arXiv", reference_role, override=True)
+    app.add_role("doi", reference_role, override=True)
+    app.add_role("DOI", reference_role, override=True)
+
+
+def setup(app):
+    app.connect("builder-inited", setup_link_role)
+    return {"version": "0.1", "parallel_read_safe": True}