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Merged
merged 17 commits into from
Oct 20, 2021

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puhuk
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@puhuk puhuk commented Sep 21, 2021

To resolve issue #21088

Reference Issues/PRs

Fixes issue #21088

What does this implement/fix? Explain your changes.

To resolve issue #21088, correct references to use :doi: and :arxiv:

Any other comments?

@@ -247,7 +247,7 @@ or store training examples.
<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>`_
<:doi:`10.1145/2487575.2487591`>`_
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Nesting backticks is invalid in restructured text format. See the rendered HTML page here:

https://153542-843222-gh.circle-artifacts.com/0/doc/modules/kernel_approximation.html

(you can access it via the ci/circleci: doc artifact entry of the continuous integration status report on the main github page of the PR).

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Also, you can test the rendering of the website (without running all the examples) on your local machine with

cd doc
EXAMPLES_PATTERN=disabled make html

see https://scikit-learn.org/dev/developers/contributing.html#documentation for the details and the list of dependencies.

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@puhuk puhuk Sep 23, 2021

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Thanks for the feedback. Let me check and re-send the PR.

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Note that I did the comment specifically for kernel_approximation but this is a general rule that applies for all the other links.

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See the rendered HTML in the ci/circleci: doc artifact entry of the "checks" running on the PR.

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Is this issue still open?
I want to take it!

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puhuk commented Sep 29, 2021

@ogrisel Could you review this PR :) Thanks in advance.

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Thanks @puhuk. Please check how doi and arxiv directives should be used to correctly link to the references.

doc/about.rst Outdated
@@ -86,7 +86,7 @@ If you want to cite scikit-learn for its API or design, you may also want to con
following paper:

`API design for machine learning software: experiences from the scikit-learn
project <https://arxiv.org/abs/1309.0238>`_, Buitinck *et al.*, 2013.
project <:arxiv:`1309.0238`>`_, Buitinck *et al.*, 2013.
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Suggested change
project <:arxiv:`1309.0238`>`_, Buitinck *et al.*, 2013.
project :arxiv:`1309.0238`, Buitinck *et al.*, 2013.

If you check the rendering of this pull request you see that the references are not displayed correctly. You might want to check scipy/#12858 to see how doi and arxiv directives should be used. My suggestion here is an example.

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Thanks, let me check :)

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Thanks @puhuk! The failing lint check is related to the doi_role.py file. scikit-learn recently adopted black formatting (see also the pull request checklist): the file needs to be reformatted with black doi_role.py.

doc/about.rst Outdated
Comment on lines 88 to 89
API design for machine learning software: experiences from the scikit-learn
project, Buitinck *et al.*, 2013. :arxiv:`1309.0238`
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I just realize that it is possible to give an explicit caption

You can also give an explicit caption, e.g.
:doi:`Basic local alignment search tool <10.1016/S0022-2836(05)80360-2>`.

Do you mind checking this solution that would be more consistent with other references?

Suggested change
API design for machine learning software: experiences from the scikit-learn
project, Buitinck *et al.*, 2013. :arxiv:`1309.0238`
:arxiv:`API design for machine learning software: experiences from the scikit-learn
project <1309.0238>`, Buitinck *et al.*, 2013.

Thanks!

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@cmarmo Thanks for your advice! I reformatted and synced with other references.

Updated based on PR review
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Thanks @puhuk! Almost there! It seems that some references went outside your radar.... :)
LGTM once they are fixed.

@@ -1705,7 +1705,7 @@ cluster analysis.
* 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>`_.
:doi:`doi:10.1016/0377-0427(87)90125-7 <10.1016/0377-0427(87)90125-7>`
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Do you mind adding an explicit caption here too? Thanks!

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Sure, let me add :)

@@ -1805,7 +1805,7 @@ number of points in cluster :math:`q`.
`"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:`doi:10.1080/03610927408827101 <10.1080/03610927408827101>`.
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same as previous comment: the title of the article should be the text of the link.... there are some other similar cases in this file and in preprocessing.rst , with the doi directive.

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puhuk commented Oct 7, 2021

Thanks for quick feedback!
Let me check and update soon.

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Thanks @puhuk! It seems to me that everything is good now.
Perhaps @lorentzenchr who opened the issue might want to have a look?

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puhuk commented Oct 7, 2021

@cmarmo Thanks for the review and your helps :)

@lorentzenchr If you have any feedback, please just let me know to reflect to this PR :)

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Thank you, @puhuk.

This is a useful addition to the documentation setup.
Here are some minor suggestions.

Comment on lines 23 to 40
def doi_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):
text = utils.unescape(text)
has_explicit_title, title, part = split_explicit_title(text)
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 arxiv_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):
text = utils.unescape(text)
has_explicit_title, title, part = split_explicit_title(text)
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], []
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Probably, those can be factorized together?

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Maybe. Let me try and request review again.

Comment on lines 187 to 188
.. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`, Machine Learning, 45(1), 5-32,
2001.
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For visual alignment.

Suggested change
.. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`, Machine Learning, 45(1), 5-32,
2001.
.. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`,
Machine Learning, 45(1), 5-32, 2001.

Comment on lines 983 to 984
* 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).
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Suggested change
* 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).
* 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).

Comment on lines 566 to 567
.. [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.
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Suggested change
.. [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.
.. [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.

@@ -934,7 +934,7 @@ 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>`
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As this line gets modified, we can wrap it.

Suggested change
.. [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>`
.. [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>`

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@cmarmo I won‘t have time.
@jjerphan Thanks for your review. If you approve, let us count it as +1 😏

Comment on lines 23 to 39
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], []
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Can the logic be further simplified?

Suggested change
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 reference_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):
text = utils.unescape(text)
has_explicit_title, title, part = split_explicit_title(text)
typ = typ.lower()
full_url_prefixes = {
"arxiv": "https://arxiv.org/abs/",
"doi": "https://doi.org/",
}
title_prefixes = {
"arxiv": "arXiv:",
"doi": "DOI:",
}
full_url = f"{full_url_prefixes[typ]}{part}"
if not has_explicit_title:
title = f"{title_prefixes[typ]}{part}"
pnode = nodes.reference(title, title, internal=False, refuri=full_url)
return [pnode], []

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@jjerphan sorry for stepping in. It seems to me that the optimization of this piece of code, taken from the scipy implementation, is outside the scope of this pull request, labeled as documentation and good first issue. Perhaps this might be kept for a further improvement in a follow-up pull request?

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@cmarmo, Thanks for review. I like @jjerphan 's code even though it is out of scope for this PR. If you are ok let me recommit :)

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puhuk commented Oct 11, 2021

@cmarmo, @jjerphan
Sorry for bothering. But my PR has error with ci/circleci: linux-arm64.
There was no issue when build from local environment.
Could you tell me how can I resolve this.

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Hi @puhuk,

The CI error is unrelated to the latest changes starting from 8b66236.

Between dabe843 and 8b66236, changes have been made on main on the CI script (by b2ee0f4) and might cause your PR to fail.

Can you try merging main in your branch?

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puhuk commented Oct 11, 2021

@jjerphan
I try with git merge origin but it fails again.
You mean merge the main repo of 'https://github.com/scikit-learn/scikit-learn' to my branch , right?

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puhuk commented Oct 11, 2021

@jjerphan It seems done Thanks! :)

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Thanks @puhuk: it looks like there are references which could use those directives.

For instance (those lists might not be complete):

arXiv HTTP links
benchmarks/bench_plot_randomized_svd.py:55:    Halko, et al., 2009 https://arxiv.org/abs/0909.4061
doc/modules/sgd.rst:561:      <https://arxiv.org/pdf/1107.2490v2.pdf>`_
doc/modules/decomposition.rst:177:      <https://arxiv.org/pdf/1412.3510.pdf>`_
doc/modules/decomposition.rst:352:          <https://arxiv.org/pdf/1412.3510.pdf>`_
doc/modules/decomposition.rst:937:      <https://arxiv.org/pdf/1010.1763.pdf>`_
doc/modules/neural_networks_supervised.rst:372:       <https://arxiv.org/pdf/1412.6980v8.pdf>`_
doc/sphinxext/doi_role.py:27:        full_url = "https://arxiv.org/abs/" + part
doc/datasets/loading_other_datasets.rst:226:   <https://arxiv.org/pdf/1407.7722.pdf>`_,
examples/inspection/plot_partial_dependence.py:33:       Graphical Statistics, 24(1): 44-65 (https://arxiv.org/abs/1309.6392)
sklearn/cluster/_hierarchical_fast.pyx:454:# Implements MST-LINKAGE-CORE from https://arxiv.org/abs/1109.2378
sklearn/cluster/_hierarchical_fast.pyx:465:    by Daniel Mullner (https://arxiv.org/abs/1109.2378).
sklearn/cluster/_hierarchical_fast.pyx:486:        very efficiently. See https://arxiv.org/abs/1109.2378
sklearn/decomposition/_truncated_svd.py:120:    Halko, et al., 2009 (arXiv:909) https://arxiv.org/pdf/0909.4061.pdf
sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py:444:    # "Remark 3" in https://arxiv.org/abs/1902.06931
sklearn/ensemble/_hist_gradient_boosting/splitting.pyx:503:                    # https://arxiv.org/abs/1603.02754
sklearn/ensemble/_hist_gradient_boosting/splitting.pyx:1040:    https://arxiv.org/abs/1603.02754
sklearn/ensemble/_hist_gradient_boosting/splitting.pyx:1079:    https://arxiv.org/abs/1603.02754
sklearn/ensemble/_hist_gradient_boosting/splitting.pyx:1119:    https://arxiv.org/abs/1603.02754
sklearn/linear_model/tests/test_logistic.py:2204:    https://arxiv.org/pdf/1706.10272.pdf
sklearn/linear_model/_sag.py:64:    https://arxiv.org/abs/1407.0202
sklearn/linear_model/_sag.py:238:    https://arxiv.org/abs/1407.0202
sklearn/linear_model/_sag_fast.pyx.tp:253:    https://arxiv.org/abs/1407.0202
sklearn/linear_model/_logistic.py:1374:        https://arxiv.org/abs/1407.0202
sklearn/utils/extmath.py:209:    Halko, et al., 2009 (arXiv:909) https://arxiv.org/pdf/0909.4061.pdf
sklearn/utils/extmath.py:351:      Halko, et al., 2009 https://arxiv.org/abs/0909.4061
sklearn/utils/extmath.py:543:      Halko, et al., 2009 https://arxiv.org/abs/0909.4061
DOI HTTP links
doc/modules/semi_supervised.rst:74:       https://doi.org/10.3115/981658.981684
doc/sphinxext/doi_role.py:8:    (``https://doi.org/10.1016/S0022-2836(05)80360-2``).
doc/sphinxext/doi_role.py:33:        full_url = "https://doi.org/" + part
examples/linear_model/plot_poisson_regression_non_normal_loss.py:35:    <http://dx.doi.org/10.2139/ssrn.3164764>`_
examples/linear_model/plot_tweedie_regression_insurance_claims.py:37:    <http://dx.doi.org/10.2139/ssrn.3164764>`_
examples/inspection/plot_permutation_importance.py:24:       2001. https://doi.org/10.1023/A:1010933404324
examples/release_highlights/plot_release_highlights_0_24_0.py:113:# <https://doi.org/10.3115/981658.981684>`_ can now be used with any
examples/kernel_approximation/plot_scalable_poly_kernels.py:226:# https://doi.org/10.1145/2487575.2487591
sklearn/decomposition/_pca.py:309:    <https://doi.org/10.1137/090771806>`_
sklearn/decomposition/_pca.py:314:    <https://doi.org/10.1016/j.acha.2010.02.003>`_.
sklearn/linear_model/tests/test_logistic.py:2199:    https://doi.org/10.1093/biostatistics%2Fkxg046
sklearn/manifold/_spectral_embedding.py:232:      https://doi.org/10.1137%2FS1064827500366124
sklearn/manifold/_spectral_embedding.py:327:        #     see https://doi.org/10.1007/s10208-015-9297-1
sklearn/metrics/_classification.py:866:       <https://doi.org/10.1093/bioinformatics/16.5.412>`_.
sklearn/preprocessing/_polynomial.py:841:            # penalties" https://doi.org/10.1214/ss/1038425655 advice
sklearn/semi_supervised/_self_training.py:123:    https://doi.org/10.3115/981658.981684
sklearn/inspection/_permutation_importance.py:216:             2001. https://doi.org/10.1023/A:1010933404324
Other DOI references
benchmarks/bench_covertype.py:29:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.2112>`_
benchmarks/bench_covertype.py:33:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8513>`_
doc/modules/biclustering.rst:165:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3011>`__.
doc/modules/biclustering.rst:248:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.1608>`__.
doc/modules/naive_bayes.rst:219:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.1529>`_
doc/modules/naive_bayes.rst:224:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.5542>`_
doc/modules/feature_selection.rst:310:      <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.4369&rep=rep1&type=pdf>`_.
doc/modules/manifold.rst:252:     <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382>`_
doc/modules/manifold.rst:392:     <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.3693>`_
doc/modules/sgd.rst:550:      <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.74.8513>`_
doc/modules/sgd.rst:565:      <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.4696>`_
doc/modules/sgd.rst:571:      <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.7377>`_
doc/modules/neighbors.rst:348:     <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.8209>`_,
doc/modules/random_projection.rst:31:   `Random projection in dimensionality reduction: applications to image and text data. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.5135&rep=rep1&type=pdf>`_
doc/modules/random_projection.rst:87:    <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.39.3334&rep=rep1&type=pdf>`_
doc/modules/linear_model.rst:717:    * David J. C. MacKay, `Bayesian Interpolation <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.27.9072&rep=rep1&type=pdf>`_, 1992.
doc/modules/linear_model.rst:765:    .. [4] Tristan Fletcher: `Relevance Vector Machines explained <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.651.8603&rep=rep1&type=pdf>`_
doc/modules/clustering.rst:432:   <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.76.8968&rep=rep1&type=pdf>`_
doc/modules/clustering.rst:532:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323>`_
doc/modules/clustering.rst:536:   <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324>`_
doc/modules/clustering.rst:540:   <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.1501>`_
doc/modules/clustering.rst:544:   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100>`_
doc/modules/svm.rst:804:      <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.4288>`_,
doc/modules/model_evaluation.rst:832:     <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.5766&rep=rep1&type=pdf>`_,
examples/calibration/plot_compare_calibration.py:206:#        <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.29.3039&rep=rep1&type=pdf>`_
sklearn/cluster/_bicluster.py:306:      <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3011>`__.
sklearn/cluster/_bicluster.py:461:      <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.1608>`__.
sklearn/cluster/_spectral.py:254:           <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324>`_
sklearn/cluster/_spectral.py:258:           <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323>`_
sklearn/cluster/_spectral.py:489:           <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324>`_
sklearn/cluster/_spectral.py:493:           <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323>`_
sklearn/manifold/_locally_linear.py:298:        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382
sklearn/manifold/_locally_linear.py:664:        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382
sklearn/manifold/_spectral_embedding.py:491:      http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
sklearn/manifold/_spectral_embedding.py:495:      http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100
sklearn/manifold/_spectral_embedding.py:499:      http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
sklearn/mixture/_bayesian_mixture.py:320:       <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.2841&rep=rep1&type=pdf>`_
sklearn/semi_supervised/_label_propagation.py:544:    http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219
sklearn/svm/_classes.py:711:        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
sklearn/svm/_classes.py:968:        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
sklearn/svm/_classes.py:1170:        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
sklearn/svm/_classes.py:1358:        <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
sklearn/tree/_reingold_tilford.py:161:    # http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.16.8757&rep=rep1&type=pdf
sklearn/random_projection.py:116:           http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.3654

Probably, creating dedicated Sphinx entries for them in subsequent PRs might be better because this is unrelated to this PR scope.

I would wait for maintainers' comments and/or approvals.

@puhuk
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puhuk commented Oct 12, 2021

@ogrisel, Hi. If you have any other feedback with this, please just let me know to update :)

@jjerphan
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@ogrisel, Hi. If you have any other feedback with this, please just let me know to update :)

I also think @cmarmo can provide authoritative feedback.

@puhuk
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puhuk commented Oct 18, 2021

Hi @ogrisel @cmarmo @jjerphan
This is reminder for review :)
It would be thanks to get feedback with this PR.

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Let's keep this PR short and atomic: this LGTM!

As for the remarks in the latest message, I think they can be treated in another PR.

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LGTM

@@ -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>`.
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The first link, https://www.researchgate.net/publication/233096619_A_Dendrite_Method_for_Cluster_Analysis gives free access to the paper while the doi does not have public access.

Since the version on main also has both links, I am okay with your change.

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cmarmo commented Oct 19, 2021

@thomasjpfan shall we just merge this one? Thanks!

@thomasjpfan thomasjpfan changed the title DOC: use :doi: and :arxiv: directives for references DOC Use :doi: and :arxiv: directives for references Oct 20, 2021
@thomasjpfan thomasjpfan merged commit 337e0d2 into scikit-learn:main Oct 20, 2021
@glemaitre glemaitre mentioned this pull request Oct 23, 2021
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glemaitre pushed a commit to glemaitre/scikit-learn that referenced this pull request Oct 23, 2021
samronsin pushed a commit to samronsin/scikit-learn that referenced this pull request Nov 30, 2021
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7 participants