DOC add cross-reference to examples instead of duplicating content for GPR #20003

glemaitre · 2021-04-28T21:34:25Z

follow-up #19952
closes #11562

Revamp the documentation of the Gaussian Process user guide.

TODO:

rework the example to use notebook style (done in main)
remove example from user guide
add references sections
add examples links
add potential mathematical section

jjerphan

Thanks for improving those examples @glemaitre. Those are really instructive.

Here are some suggestions from a new-comer to GPR.

doc/modules/gaussian_process.rst

examples/gaussian_process/plot_compare_gpr_krr.py

doc/modules/gaussian_process.rst

examples/gaussian_process/plot_gpr_co2.py

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

github-actions · 2023-08-02T08:58:20Z

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glemaitre · 2023-08-02T08:59:39Z

ping @adrinjalali

I think the following PR addresses some of your proposals discussed IRL when reviewing #26980

adrinjalali · 2023-08-03T10:04:01Z

I like the changes, but in the listed examples, we see:

And I think the second and 4th examples don't have a title which is informative enough for users to know what to expect. We should either change the title, or have a text here before referencing them so that the reader knows what they gain by reading the example.

glemaitre · 2023-08-11T22:35:56Z

I tried to improve the titles.

adrinjalali · 2023-08-24T13:33:25Z

@noashin started reviewing this I think, I'll wait for that before doing another review.

noashin · 2023-08-24T10:38:06Z

doc/modules/gaussian_process.rst

I would strongly encourage including the term NONPARAMETRIC when discussing GPs.

I would rephrase the opening (lines 9-11):
Gaussian Processes (GP) are a nonparametric supervised learning method used
to solve regression and probabilistic classification problems.

noashin · 2023-08-24T10:51:39Z

doc/modules/gaussian_process.rst

@@ -27,8 +25,8 @@ The advantages of Gaussian processes are:

 The disadvantages of Gaussian processes include:

-    - They are not sparse, i.e., they use the whole samples/features information to


Sparse Gaussian Processes is a thing, just not in Scikit. One defines a set of inducing points (smaller than the data) and use them for learning the GP instead of the data.
This is a good blog post about it.

noashin · 2023-08-24T11:29:28Z

doc/modules/gaussian_process.rst

+
+The noise level in the targets can be specified by passing it via the parameter
+`alpha`, either globally as a scalar or per datapoint. Note that a moderate
+noise level can also be helpful for dealing with numeric issues during fitting


"numerical instabilities" instead of "numerical issues" ?

glemaitre · 2023-08-29T15:10:02Z

Thanks @noashin. I added the suggestions.

adrinjalali

Are you gonna do another PR for GPC?

Otherwise LGTM.

adrinjalali · 2023-09-11T11:02:40Z

doc/modules/gaussian_process.rst

@@ -77,149 +88,12 @@ the API of standard scikit-learn estimators, GaussianProcessRegressor:
  externally for other ways of selecting hyperparameters, e.g., via
  Markov chain Monte Carlo.

+.. topic:: Examples


maybe rename to gpr_examples? we might easily conflict with another place defining an Examples topic.

This is a topic and not the name of the section. I don't think you can use this to reference.

glemaitre · 2023-09-11T11:52:00Z

Are you gonna do another PR for GPC?

Yes. It would be in the same spirit than what we did for GPR.

jjerphan

Thank you, @glemaitre.

lesteve · 2023-09-12T09:04:00Z

No idea why but one Azure build was timing out, since this is a doc PR, I merged it manually.

…r GPR (scikit-learn#20003) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

…r GPR (#20003) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

…r GPR (scikit-learn#20003) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

DOC revamp documentation of GPR

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Update examples/gaussian_process/plot_compare_gpr_krr.py

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Update examples/gaussian_process/plot_gpr_noisy_targets.py

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DOC add cross-reference to examples instead of duplicating content for GPR #20003

DOC add cross-reference to examples instead of duplicating content for GPR #20003

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		@@ -27,8 +25,8 @@ The advantages of Gaussian processes are:

		The disadvantages of Gaussian processes include:

		- They are not sparse, i.e., they use the whole samples/features information to

DOC add cross-reference to examples instead of duplicating content for GPR #20003

DOC add cross-reference to examples instead of duplicating content for GPR #20003

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